From 9273bdece7a07b95332881d8ddc6bb3fbfcda6ef Mon Sep 17 00:00:00 2001 From: LJones-alt Date: Thu, 25 Sep 2025 10:55:52 +0100 Subject: [PATCH 01/15] 93% policy --- .gitignore | 8 + docker/.env.ros2 | 5 +- docker/Dockerfile.base | 73 +- docker/Dockerfile.ros2 | 2 +- docker/docker-compose.yaml | 14 + docs/beaker.obj | 3 + docs/clean_urdf.py | 32 + docs/macros_private.py | 27 + .../bcq/20250721123120/config.json | 315 ++++++ .../bc_new_demos/20250924112632/config.json | 216 ++++ .../bc_new_demos/20250924123612/config.json | 216 ++++ .../bc_trans/20250715154020/config.json | 216 ++++ .../bc_trans/20250721074506/config.json | 216 ++++ .../bc_trans/20250721074750/config.json | 216 ++++ .../bc_trans/20250721085238/config.json | 216 ++++ .../bcq/20250721135829/config.json | 315 ++++++ .../franks_stack/20250715152538/config.json | 210 ++++ .../franks_stack/20250716100129/config.json | 210 ++++ .../isaaclab_mimic/generate_dataset.py | 3 +- .../robomimic/evaluation.py | 206 ++++ .../robomimic/play_ensemble.py | 968 ++++++++++++++++++ .../robomimic/train_ensemble.py | 532 ++++++++++ scripts/tools/convert_mesh.py | 2 +- source/isaaclab/isaaclab/envs/mdp/events.py | 3 +- .../isaaclab_mimic/envs/__init__.py | 24 +- .../envs/cube_blueprint_mimic_env_cfg.py | 150 +++ .../isaaclab_mimic/envs/cube_mimic_env.py | 165 +++ .../isaaclab_mimic/envs/cube_mimic_env_cfg.py | 132 +++ .../curobo/curobo_planner_cfg.py | 24 +- .../motion_planners/motion_planner_base.py | 2 +- .../manipulation/beaker_pour/__init__.py | 9 + .../beaker_pour/config/__init__.py | 9 + .../beaker_pour/config/franka/__init__.py | 74 ++ .../config/franka/agents/__init__.py | 4 + .../franka/agents/rl_games_ppo_cfg.yaml | 79 ++ .../config/franka/agents/robomimic/bc.json | 135 +++ .../agents/robomimic/bc_rnn_low_dim.json | 135 +++ .../agents/robomimic/bc_rnn_low_dim_not.json | 306 ++++++ .../franka/agents/robomimic/bc_trans.json | 133 +++ .../config/franka/agents/robomimic/bcq.json | 308 ++++++ .../config/franka/agents/robomimic/hbc.json | 306 ++++++ .../config/franka/agents/rsl_rl_ppo_cfg.py | 37 + .../config/franka/agents/sb3_ppo_cfg.yaml | 28 + .../config/franka/agents/skrl_ppo_cfg.yaml | 80 ++ .../beaker_pour/config/franka/dev_osc_cfg.py | 114 +++ .../config/franka/ik_abs_env_cfg.py | 128 +++ .../config/franka/ik_rel_env_cfg.py | 65 ++ .../config/franka/joint_pos_env_cfg.py | 102 ++ .../beaker_pour/config/franka/pour_env_cfg.py | 132 +++ .../config/franka/pour_ik_rel_env_cfg.py | 63 ++ .../config/franka/pour_ik_rel_env_obs.py | 49 + .../manipulation/beaker_pour/mdp/__init__.py | 12 + .../beaker_pour/mdp/observations.py | 289 ++++++ .../manipulation/beaker_pour/mdp/rewards.py | 73 ++ .../beaker_pour/mdp/terminations.py | 97 ++ .../manipulation/beaker_pour/pour_env_cfg.py | 361 +++++++ .../manipulation/cube_lift/__init__.py | 9 + .../manipulation/cube_lift/config/__init__.py | 9 + .../cube_lift/config/franka/__init__.py | 141 +++ .../config/franka/agents/__init__.py | 4 + .../franka/agents/rl_games_ppo_cfg.yaml | 79 ++ .../config/franka/agents/robomimic/bc.json | 135 +++ .../agents/robomimic/bc_rnn_low_dim.json | 135 +++ .../agents/robomimic/bc_rnn_low_dim_not.json | 306 ++++++ .../franka/agents/robomimic/bc_trans.json | 133 +++ .../config/franka/agents/robomimic/bcq.json | 308 ++++++ .../config/franka/agents/robomimic/hbc.json | 306 ++++++ .../config/franka/agents/rsl_rl_ppo_cfg.py | 37 + .../config/franka/agents/sb3_ppo_cfg.yaml | 28 + .../config/franka/agents/skrl_ppo_cfg.yaml | 80 ++ .../cube_lift/config/franka/dev_env_cfg.py | 132 +++ .../config/franka/dev_ik_rel_env_cfg.py | 63 ++ .../config/franka/dev_ik_rel_env_obs.py | 49 + .../cube_lift/config/franka/dev_osc_cfg.py | 114 +++ .../cube_lift/config/franka/ik_abs_env_cfg.py | 128 +++ .../cube_lift/config/franka/ik_rel_env_cfg.py | 65 ++ .../config/franka/joint_pos_env_cfg.py | 102 ++ .../manipulation/cube_lift/lift_env_cfg.py | 362 +++++++ .../manipulation/cube_lift/mdp/__init__.py | 12 + .../cube_lift/mdp/observations.py | 309 ++++++ .../manipulation/cube_lift/mdp/rewards.py | 73 ++ .../cube_lift/mdp/terminations.py | 97 ++ 82 files changed, 11051 insertions(+), 14 deletions(-) create mode 100644 docs/beaker.obj create mode 100644 docs/clean_urdf.py create mode 100644 docs/macros_private.py create mode 100755 docs/training_runs/bc_q/Dev-IK-Rel-v0/bcq/20250721123120/config.json create mode 100644 docs/training_runs/bc_rnn_ensemble_pick_place_new_start/Dev-IK-Rel-v0/model0/bc_new_demos/20250924112632/config.json create mode 100644 docs/training_runs/bc_rnn_ensemble_pick_place_new_start/Dev-IK-Rel-v0/model0/bc_new_demos/20250924123612/config.json create mode 100755 docs/training_runs/bc_trans/bc_trans/20250715154020/config.json create mode 100755 docs/training_runs/bc_trans/bc_trans/20250721074506/config.json create mode 100755 docs/training_runs/bc_trans/bc_trans/20250721074750/config.json create mode 100755 docs/training_runs/bc_trans/bc_trans/20250721085238/config.json create mode 100755 docs/training_runs/bcq/Dev-IK-Rel-v0/bcq/20250721135829/config.json create mode 100755 docs/training_runs/franks_stack/20250715152538/config.json create mode 100755 docs/training_runs/franks_stack/20250716100129/config.json create mode 100644 scripts/imitation_learning/robomimic/evaluation.py create mode 100644 scripts/imitation_learning/robomimic/play_ensemble.py create mode 100644 scripts/imitation_learning/robomimic/train_ensemble.py create mode 100755 source/isaaclab_mimic/isaaclab_mimic/envs/cube_blueprint_mimic_env_cfg.py create mode 100755 source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env.py create mode 100755 source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/__init__.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/__init__.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/__init__.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/__init__.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/rl_games_ppo_cfg.yaml create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/bc.json create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/bc_rnn_low_dim.json create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/bc_rnn_low_dim_not.json create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/bc_trans.json create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/bcq.json create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/hbc.json create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/rsl_rl_ppo_cfg.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/sb3_ppo_cfg.yaml create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/skrl_ppo_cfg.yaml create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/dev_osc_cfg.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/ik_abs_env_cfg.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/ik_rel_env_cfg.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/joint_pos_env_cfg.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/pour_env_cfg.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/pour_ik_rel_env_cfg.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/pour_ik_rel_env_obs.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/mdp/__init__.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/mdp/observations.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/mdp/rewards.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/mdp/terminations.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/pour_env_cfg.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/__init__.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/__init__.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/__init__.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/rl_games_ppo_cfg.yaml create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc.json create mode 100755 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mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/__init__.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/rewards.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/terminations.py diff --git a/.gitignore b/.gitignore index 08d2e8dee5a9..18d749f04b96 100644 --- a/.gitignore +++ b/.gitignore @@ -13,6 +13,12 @@ **/*.usda **/*.usdc **/*.usdz +**/*.urdf + +#no path files +**/*.pth +**/*.hdf5 +**/*.txt # Python .DS_Store @@ -63,6 +69,8 @@ _build # Teleop Recorded Dataset /datasets/ +/franka_robotiq/ +/source/isaaclab_assets/ # Tests tests/ diff --git a/docker/.env.ros2 b/docker/.env.ros2 index 609704f45674..ebb840428a95 100644 --- a/docker/.env.ros2 +++ b/docker/.env.ros2 @@ -8,7 +8,4 @@ RMW_IMPLEMENTATION=rmw_fastrtps_cpp # Path to fastdds.xml file to use (only needed when using fastdds) FASTRTPS_DEFAULT_PROFILES_FILE=${DOCKER_USER_HOME}/.ros/fastdds.xml # Path to cyclonedds.xml file to use (only needed when using cyclonedds) -CYCLONEDDS_URI=${DOCKER_USER_HOME}/.ros/cyclonedds.xml -# Docker image and container name suffix (default "", set by the container_interface.py script) -# Example: "-custom" -DOCKER_NAME_SUFFIX="" +CYCLONEDDS_URI=${DOCKER_USER_HOME}/.ros/cyclonedds.xml \ No newline at end of file diff --git a/docker/Dockerfile.base b/docker/Dockerfile.base index 9aff6b165c93..4ebf8291ca03 100644 --- a/docker/Dockerfile.base +++ b/docker/Dockerfile.base @@ -93,7 +93,35 @@ RUN --mount=type=cache,target=${DOCKER_USER_HOME}/.cache/pip \ # HACK: Remove install of quadprog dependency RUN ${ISAACLAB_PATH}/isaaclab.sh -p -m pip uninstall -y quadprog - +RUN set -euo pipefail && \ + . /etc/os-release && \ + case "$ID" in \ + ubuntu) \ + case "$VERSION_ID" in \ + "20.04") cuda_repo="ubuntu2004";; \ + "22.04") cuda_repo="ubuntu2204";; \ + "24.04") cuda_repo="ubuntu2404";; \ + *) echo "Unsupported Ubuntu $VERSION_ID"; exit 1;; \ + esac ;; \ + *) echo "Unsupported base OS: $ID"; exit 1 ;; \ + esac && \ + apt-get update && apt-get install -y --no-install-recommends wget gnupg ca-certificates && \ + wget -q https://developer.download.nvidia.com/compute/cuda/repos/${cuda_repo}/x86_64/cuda-keyring_1.1-1_all.deb && \ + dpkg -i cuda-keyring_1.1-1_all.deb && \ + rm -f cuda-keyring_1.1-1_all.deb && \ + wget -q https://developer.download.nvidia.com/compute/cuda/repos/${cuda_repo}/x86_64/cuda-${cuda_repo}.pin && \ + mv cuda-${cuda_repo}.pin /etc/apt/preferences.d/cuda-repository-pin-600 && \ + apt-get update && \ + apt-get install -y --no-install-recommends cuda-toolkit-12-8 && \ + apt-get -y autoremove && apt-get clean && rm -rf /var/lib/apt/lists/* + + +ENV CUDA_HOME=/usr/local/cuda-12.8 +ENV PATH=${CUDA_HOME}/bin:${PATH} +ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH} +ENV TORCH_CUDA_ARCH_LIST=8.0+PTX +RUN ${ISAACLAB_PATH}/isaaclab.sh -p -m pip install --no-build-isolation \ + "nvidia-curobo @ git+https://github.com/NVlabs/curobo.git@ebb71702f3f70e767f40fd8e050674af0288abe8" # aliasing isaaclab.sh and python for convenience RUN echo "export ISAACLAB_PATH=${ISAACLAB_PATH}" >> ${HOME}/.bashrc && \ echo "alias isaaclab=${ISAACLAB_PATH}/isaaclab.sh" >> ${HOME}/.bashrc && \ @@ -106,6 +134,49 @@ RUN echo "export ISAACLAB_PATH=${ISAACLAB_PATH}" >> ${HOME}/.bashrc && \ echo "shopt -s histappend" >> /root/.bashrc && \ echo "PROMPT_COMMAND='history -a'" >> /root/.bashrc + # use pip caching to avoid reinstalling large packages +#RUN --mount=type=cache,target=${DOCKER_USER_HOME}/.cache/pip \ +# pip install --upgrade --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128 +# install TensorFlow and nightly PyTorch +RUN --mount=type=cache,target=${DOCKER_USER_HOME}/.cache/pip \ + ${ISAACLAB_PATH}/_isaac_sim/python.sh -m pip install --upgrade --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128 + +RUN --mount=type=cache,target=${DOCKER_USER_HOME}/.cache/pip \ + ${ISAACLAB_PATH}/_isaac_sim/python.sh -m pip install tensorflow scikit-learn + +RUN --mount=type=cache,target=${DOCKER_USER_HOME}/.cache/pip \ +${ISAACLAB_PATH}/_isaac_sim/python.sh -m pip install protobuf==3.20.2 +# Detect Ubuntu version and install CUDA 12.8 via NVIDIA network repo (cuda-keyring) +RUN set -euo pipefail && \ + . /etc/os-release && \ + case "$ID" in \ + ubuntu) \ + case "$VERSION_ID" in \ + "20.04") cuda_repo="ubuntu2004";; \ + "22.04") cuda_repo="ubuntu2204";; \ + "24.04") cuda_repo="ubuntu2404";; \ + *) echo "Unsupported Ubuntu $VERSION_ID"; exit 1;; \ + esac ;; \ + *) echo "Unsupported base OS: $ID"; exit 1 ;; \ + esac && \ + apt-get update && apt-get install -y --no-install-recommends wget gnupg ca-certificates && \ + wget -q https://developer.download.nvidia.com/compute/cuda/repos/${cuda_repo}/x86_64/cuda-keyring_1.1-1_all.deb && \ + dpkg -i cuda-keyring_1.1-1_all.deb && \ + rm -f cuda-keyring_1.1-1_all.deb && \ + wget -q https://developer.download.nvidia.com/compute/cuda/repos/${cuda_repo}/x86_64/cuda-${cuda_repo}.pin && \ + mv cuda-${cuda_repo}.pin /etc/apt/preferences.d/cuda-repository-pin-600 && \ + apt-get update && \ + apt-get install -y --no-install-recommends cuda-toolkit-12-8 && \ + apt-get -y autoremove && apt-get clean && rm -rf /var/lib/apt/lists/* + + +ENV CUDA_HOME=/usr/local/cuda-12.8 +ENV PATH=${CUDA_HOME}/bin:${PATH} +ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH} +ENV TORCH_CUDA_ARCH_LIST=8.0+PTX + +# Copy the Isaac Lab directory (files to exclude are defined in .dockerignore) +COPY ../ ${ISAACLAB_PATH} # make working directory as the Isaac Lab directory # this is the default directory when the container is run WORKDIR ${ISAACLAB_PATH} diff --git a/docker/Dockerfile.ros2 b/docker/Dockerfile.ros2 index 2e00fc7ec396..dfc555ce31c2 100644 --- a/docker/Dockerfile.ros2 +++ b/docker/Dockerfile.ros2 @@ -4,7 +4,7 @@ # What is the docker name suffix for the base image to load? (defaults to empty string) ARG DOCKER_NAME_SUFFIX="" -FROM isaac-lab-base${DOCKER_NAME_SUFFIX} AS ros2 +FROM isaac-lab-base AS ros2 # Which ROS2 apt package to install ARG ROS2_APT_PACKAGE diff --git a/docker/docker-compose.yaml b/docker/docker-compose.yaml index d7809109bdc1..cde0175a7996 100644 --- a/docker/docker-compose.yaml +++ b/docker/docker-compose.yaml @@ -69,6 +69,10 @@ x-default-isaac-lab-volumes: &default-isaac-lab-volumes - type: bind source: .isaac-lab-docker-history target: ${DOCKER_USER_HOME}/.bash_history + - type: bind + source: /usr/share/nvidia/nvoptix.bin + target: /usr/share/nvidia/nvoptix.bin + read_only: true x-default-isaac-lab-environment: &default-isaac-lab-environment - ISAACSIM_PATH=${DOCKER_ISAACLAB_PATH}/_isaac_sim @@ -106,6 +110,10 @@ services: entrypoint: bash stdin_open: true tty: true + devices: + - /dev/hidraw0:/dev/hidraw0 + - /dev/hidraw1:/dev/hidraw1 + # - /dev/hidraw2:/dev/hidraw2 # This service adds a ROS2 Humble # installation on top of the base image @@ -135,6 +143,10 @@ services: entrypoint: bash stdin_open: true tty: true + devices: + - /dev/hidraw0:/dev/hidraw0 + - /dev/hidraw1:/dev/hidraw1 + # - /dev/hidraw2:/dev/hidraw2 volumes: # isaac-sim @@ -151,3 +163,5 @@ volumes: isaac-lab-docs: isaac-lab-logs: isaac-lab-data: + isaac-lan-ros2: + diff --git a/docs/beaker.obj b/docs/beaker.obj new file mode 100644 index 000000000000..7411fe4f313c --- /dev/null +++ b/docs/beaker.obj @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:49897da923686f624068a3611160d539b951180282ffb449c94b36c215a4d667 +size 861347 diff --git a/docs/clean_urdf.py b/docs/clean_urdf.py new file mode 100644 index 000000000000..6ef51d77a3e2 --- /dev/null +++ b/docs/clean_urdf.py @@ -0,0 +1,32 @@ +import xml.etree.ElementTree as ET + +# Load the original URDF +input_urdf = "franka_robotiq/urdf/panda_robotiq.urdf" +output_urdf = "franka_robotiq/urdf/lula_franka_isaaclab.urdf" + +tree = ET.parse(input_urdf) +robot = tree.getroot() + +# Default inertial parameters +DEFAULT_MASS = 1.0 +DEFAULT_INERTIA = {"ixx": 0.01, "iyy": 0.01, "izz": 0.01, "ixy": 0.0, "ixz": 0.0, "iyz": 0.0} + +for link in robot.findall("link"): + # Add inertial if missing + if link.find("inertial") is None: + inertial = ET.SubElement(link, "inertial") + ET.SubElement(inertial, "origin", xyz="0 0 0", rpy="0 0 0") + ET.SubElement(inertial, "mass", value=str(DEFAULT_MASS)) + inertia_attrib = {k: str(v) for k, v in DEFAULT_INERTIA.items()} + ET.SubElement(inertial, "inertia", **inertia_attrib) + + # Add simple box collision if missing + if link.find("collision") is None: + collision = ET.SubElement(link, "collision") + geometry = ET.SubElement(collision, "geometry") + ET.SubElement(geometry, "box", size="0.05 0.05 0.05") + +# Write the cleaned URDF +tree.write(output_urdf, encoding="utf-8", xml_declaration=True) + +print(f"Cleaned URDF written to {output_urdf}") \ No newline at end of file diff --git a/docs/macros_private.py b/docs/macros_private.py new file mode 100644 index 000000000000..2760b28817b8 --- /dev/null +++ b/docs/macros_private.py @@ -0,0 +1,27 @@ +""" +Set of global variables shared across robomimic +""" +# Sets debugging mode. Should be set at top-level script so that internal +# debugging functionalities are made active +DEBUG = False + +# Whether to visualize the before & after of an observation randomizer +VISUALIZE_RANDOMIZER = False + +# wandb entity (eg. username or team name) +WANDB_ENTITY = "aarch-uol" + +# wandb api key (obtain from https://wandb.ai/authorize) +# alternatively, set up wandb from terminal with `wandb login` +WANDB_API_KEY = "f1bb6b7a94c7431472882c94835036d0156eb7b6" + +try: + from robomimic.macros_private import * +except ImportError: + from robomimic.utils.log_utils import log_warning + import robomimic + log_warning( + "No private macro file found!"\ + "\nIt is recommended to use a private macro file"\ + "\nTo setup, run: python {}/scripts/setup_macros.py".format(robomimic.__path__[0]) + ) diff --git a/docs/training_runs/bc_q/Dev-IK-Rel-v0/bcq/20250721123120/config.json b/docs/training_runs/bc_q/Dev-IK-Rel-v0/bcq/20250721123120/config.json new file mode 100755 index 000000000000..d5a1cf7e95f4 --- /dev/null +++ b/docs/training_runs/bc_q/Dev-IK-Rel-v0/bcq/20250721123120/config.json @@ -0,0 +1,315 @@ +{ + "algo_name": "bcq", + "experiment": { + "name": "bcq", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "pick-place-tabletop" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 100, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": false + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": null, + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": "docs/varying_beaker_scale_generated2_split.hdf5", + "output_dir": "/workspace/isaaclab/docs/training_runs/bc_q/Dev-IK-Rel-v0", + "num_data_workers": 1, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": true, + "hdf5_normalize_obs": false, + "hdf5_filter_key": "train", + "hdf5_validation_filter_key": "valid", + "seq_length": 1, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 1 + }, + "algo": { + "optim_params": { + "critic": { + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.1, + "epoch_schedule": [] + }, + "regularization": { + "L2": 0.0 + }, + "start_epoch": -1, + "end_epoch": -1 + }, + "action_sampler": { + "learning_rate": { + "initial": 0.001, + "decay_factor": 0.1, + "epoch_schedule": [] + }, + "regularization": { + "L2": 0.0 + }, + "start_epoch": -1, + "end_epoch": -1 + }, + "actor": { + "learning_rate": { + "initial": 0.001, + "decay_factor": 0.1, + "epoch_schedule": [] + }, + "regularization": { + "L2": 0.0 + }, + "start_epoch": -1, + "end_epoch": -1 + } + }, + "discount": 0.99, + "n_step": 1, + "target_tau": 0.005, + "infinite_horizon": false, + "critic": { + "use_huber": false, + "max_gradient_norm": null, + "value_bounds": null, + "num_action_samples": 10, + "num_action_samples_rollout": 100, + "ensemble": { + "n": 2, + "weight": 0.75 + }, + "distributional": { + "enabled": false, + "num_atoms": 51 + }, + "layer_dims": [ + 300, + 400 + ] + }, + "action_sampler": { + "actor_layer_dims": [ + 1024, + 1024 + ], + "gmm": { + "enabled": true, + "num_modes": 10, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": true, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "freeze_encoder_epoch": -1 + }, + "actor": { + "enabled": false, + "perturbation_scale": 0.05, + "layer_dims": [ + 300, + 400 + ] + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object_position", + "target_object_position", + "joint_pos", + "joint_vel", + "actions" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "flatten": true, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0, + "output_variance": false + } + }, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": { + "crop_height": 76, + "crop_width": 76, + "num_crops": 1, + "pos_enc": false + } + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "flatten": true, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0, + "output_variance": false + } + }, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": { + "crop_height": 76, + "crop_width": 76, + "num_crops": 1, + "pos_enc": false + } + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": { + "feature_dimension": 64, + "flatten": true, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0, + "output_variance": false + }, + "conv_activation": "relu", + "conv_kwargs": { + "out_channels": [ + 32, + 64, + 64 + ], + "kernel_size": [ + 8, + 4, + 2 + ], + "stride": [ + 4, + 2, + 1 + ] + } + }, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": { + "crop_height": 76, + "crop_width": 76, + "num_crops": 1, + "pos_enc": false + } + } + } + }, + "meta": { + "hp_base_config_file": null, + "hp_keys": [], + "hp_values": [] + } +} \ No newline at end of file diff --git a/docs/training_runs/bc_rnn_ensemble_pick_place_new_start/Dev-IK-Rel-v0/model0/bc_new_demos/20250924112632/config.json b/docs/training_runs/bc_rnn_ensemble_pick_place_new_start/Dev-IK-Rel-v0/model0/bc_new_demos/20250924112632/config.json new file mode 100644 index 000000000000..a6b76e34fba4 --- /dev/null +++ b/docs/training_runs/bc_rnn_ensemble_pick_place_new_start/Dev-IK-Rel-v0/model0/bc_new_demos/20250924112632/config.json @@ -0,0 +1,216 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_new_demos", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "Lift_beaker_new_demos" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": null, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": true, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": "Dev-IK-Rel-v0", + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": "docs/beaker_lift_new_start_skillgen_generated_split_normalized.hdf5", + "output_dir": "/workspace/isaaclab/logs/docs/training_runs/bc_rnn_ensemble_pick_place_new_start/Dev-IK-Rel-v0/model14/", + "num_data_workers": 1, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": "train", + "hdf5_validation_filter_key": "valid", + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.01, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gaussian": { + "enabled": false, + "fixed_std": false, + "init_std": 0.1, + "min_std": 0.01, + "std_activation": "softplus", + "low_noise_eval": true + }, + "gmm": { + "enabled": true, + "num_modes": 10, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object_position", + "target_object_position", + "joint_pos", + "joint_vel", + "actions" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + }, + "meta": { + "hp_base_config_file": null, + "hp_keys": [], + "hp_values": [] + } +} \ No newline at end of file diff --git a/docs/training_runs/bc_rnn_ensemble_pick_place_new_start/Dev-IK-Rel-v0/model0/bc_new_demos/20250924123612/config.json b/docs/training_runs/bc_rnn_ensemble_pick_place_new_start/Dev-IK-Rel-v0/model0/bc_new_demos/20250924123612/config.json new file mode 100644 index 000000000000..a6b76e34fba4 --- /dev/null +++ b/docs/training_runs/bc_rnn_ensemble_pick_place_new_start/Dev-IK-Rel-v0/model0/bc_new_demos/20250924123612/config.json @@ -0,0 +1,216 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_new_demos", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "Lift_beaker_new_demos" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": null, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": true, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": "Dev-IK-Rel-v0", + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": "docs/beaker_lift_new_start_skillgen_generated_split_normalized.hdf5", + "output_dir": "/workspace/isaaclab/logs/docs/training_runs/bc_rnn_ensemble_pick_place_new_start/Dev-IK-Rel-v0/model14/", + "num_data_workers": 1, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": "train", + "hdf5_validation_filter_key": "valid", + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.01, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gaussian": { + "enabled": false, + "fixed_std": false, + "init_std": 0.1, + "min_std": 0.01, + "std_activation": "softplus", + "low_noise_eval": true + }, + "gmm": { + "enabled": true, + "num_modes": 10, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "rnn": { + "enabled": true, 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"pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 1 + }, + "algo": { + "optim_params": { + "critic": { + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.1, + "epoch_schedule": [] + }, + "regularization": { + "L2": 0.0 + }, + "start_epoch": -1, + "end_epoch": -1 + }, + "action_sampler": { + "learning_rate": { + "initial": 0.001, + "decay_factor": 0.1, + "epoch_schedule": [] + }, + "regularization": { + "L2": 0.0 + }, + "start_epoch": -1, + "end_epoch": -1 + }, + "actor": { + "learning_rate": { + "initial": 0.001, + "decay_factor": 0.1, + "epoch_schedule": [] + }, + "regularization": { + "L2": 0.0 + }, + "start_epoch": -1, + "end_epoch": -1 + } + }, + "discount": 0.99, + "n_step": 1, + "target_tau": 0.005, + "infinite_horizon": false, + "critic": { + "use_huber": false, + "max_gradient_norm": null, + 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"learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0, + "output_variance": false + } + }, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": { + "crop_height": 76, + "crop_width": 76, + "num_crops": 1, + "pos_enc": false + } + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "flatten": true, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0, + "output_variance": false + } + }, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": { + "crop_height": 76, + "crop_width": 76, + "num_crops": 1, + "pos_enc": false + } + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": { + "feature_dimension": 64, + "flatten": true, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0, + "output_variance": false + }, + "conv_activation": "relu", + "conv_kwargs": { + "out_channels": [ + 32, + 64, + 64 + ], + "kernel_size": [ + 8, + 4, + 2 + ], + "stride": [ + 4, + 2, + 1 + ] + } + }, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": { + "crop_height": 76, + "crop_width": 76, + "num_crops": 1, + "pos_enc": false + } + } + } + }, + "meta": { + "hp_base_config_file": null, + "hp_keys": [], + "hp_values": [] + } +} \ No newline at end of file diff --git a/docs/training_runs/franks_stack/20250715152538/config.json b/docs/training_runs/franks_stack/20250715152538/config.json new file mode 100755 index 000000000000..bd1627e755cb --- /dev/null +++ b/docs/training_runs/franks_stack/20250715152538/config.json @@ -0,0 +1,210 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_rnn_low_dim_franka_stack", + "validate": false, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + 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10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.001, + "decay_factor": 0.1, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gaussian": { + "enabled": false, + "fixed_std": false, + "init_std": 0.1, + "min_std": 0.01, + "std_activation": "softplus", + "low_noise_eval": true + }, + "gmm": { + "enabled": true, + "num_modes": 5, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + }, + "meta": { + "hp_base_config_file": null, + "hp_keys": [], + "hp_values": [] + } +} \ No newline at end of file diff --git a/docs/training_runs/franks_stack/20250716100129/config.json b/docs/training_runs/franks_stack/20250716100129/config.json new file mode 100755 index 000000000000..5fa8cd807083 --- /dev/null +++ b/docs/training_runs/franks_stack/20250716100129/config.json @@ -0,0 +1,210 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_rnn_low_dim_franka_stack", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": false, + "wandb_proj_name": "debug" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 100, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": null, + "additional_envs": null, + "render": false, + "render_video": false, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": "./docs/training_data/generated_dataset_split.hdf5", + "output_dir": "/logs/docs/Models/bc/model8/Isaac-Stack-Cube-Franka-IK-Rel-v0", + "num_data_workers": 4, + "hdf5_cache_mode": "all", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": "train", + "hdf5_validation_filter_key": "valid", + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 4000, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.001, + "decay_factor": 0.1, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gaussian": { + "enabled": false, + "fixed_std": false, + "init_std": 0.1, + "min_std": 0.01, + "std_activation": "softplus", + "low_noise_eval": true + }, + "gmm": { + "enabled": true, + "num_modes": 5, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + }, + "meta": { + "hp_base_config_file": null, + "hp_keys": [], + "hp_values": [] + } +} \ No newline at end of file diff --git a/scripts/imitation_learning/isaaclab_mimic/generate_dataset.py b/scripts/imitation_learning/isaaclab_mimic/generate_dataset.py index a260151f4b15..b3bdfe03d624 100644 --- a/scripts/imitation_learning/isaaclab_mimic/generate_dataset.py +++ b/scripts/imitation_learning/isaaclab_mimic/generate_dataset.py @@ -146,7 +146,8 @@ def main(): env=env, robot=env.scene["robot"], config=planner_config, # Pass the config object - env_id=env_id, # Pass environment ID + env_id=env_id, # Pass environment ID, + #debug=True ) env.cfg.datagen_config.use_skillgen = True diff --git a/scripts/imitation_learning/robomimic/evaluation.py b/scripts/imitation_learning/robomimic/evaluation.py new file mode 100644 index 000000000000..ec2ff30d7338 --- /dev/null +++ b/scripts/imitation_learning/robomimic/evaluation.py @@ -0,0 +1,206 @@ +# run this script: ./isaaclab.sh -p scripts/imitation_learning/robomimic//evaluation.py --checkpoint ./docs/Models/model_epoch_5000.pth + +import argparse +import copy +import gymnasium as gym +import numpy as np + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import robomimic.utils.file_utils as FileUtils +import robomimic.utils.torch_utils as TorchUtils + +import time + + +# add argparse arguments +# parser = argparse.ArgumentParser(description="Evaluate robomimic policy for Isaac Lab environment.") +# parser.add_argument("--checkpoint", type=str, default=None, help="Pytorch model checkpoint to load.") +# parser.add_argument("--seed", type=int, default=101, help="Random seed.") + + + +# args_cli = parser.parse_args() + +def ensemble_uncertainty(ensemble, obs): + start_time = time.time() + outputs = [torch.from_numpy(policy(obs)) for policy in ensemble] + + outputs = torch.stack(outputs) + + mean_action = torch.mean(outputs, dim=0) + + end_time = time.time() + time_taken = end_time - start_time + + median_action = torch.median(outputs, dim=0).values + + + min_action = torch.min(outputs, dim=0) + max_action = torch.max(outputs, dim=0) + + std = torch.std(outputs, dim=0) + variance = std.pow(2) + + # std per policy (overall std across joints for each policy's action) + per_policy_std = torch.std(outputs, dim=1) + min_std_index = torch.argmin(per_policy_std) + min_std_action = outputs[min_std_index] + + return { + 'mean': mean_action, + 'median': median_action, + 'min': min_action, + 'max': max_action, + 'std': std, + 'variance': variance, + 'min_std_action': min_std_action, + 'time_taken': time_taken + } + +def MC_dropout_uncertainty(policy, obs, niters=50): + actions = [torch.from_numpy(policy(obs)) for i in range(niters)] + actions = torch.stack(actions) + + mean = torch.mean(actions, dim=0) + std = torch.std(actions, dim=0) + variance = torch.sqrt(std) + + return { + 'mean': mean, + 'std': std, + 'variance': variance + } + + +def mc_dropout_uncertainty_eval(policy, obs, niters=50): + policy.policy.nets['policy'].train() + + + actions = [] + with torch.no_grad(): + for _ in range(niters): + action = torch.from_numpy(policy(obs)) + actions.append(action) + + actions = torch.stack(actions) + + mean = torch.mean(actions, dim=0) + std = torch.std(actions, dim=0) + variance = torch.var(actions, dim=0) + + policy.policy.nets['policy'].eval() + + return { + 'mean': mean, + 'std': std, + 'variance': variance + } + + + + +def remove_dropout_layers(hooks): + for hook in hooks: + hook.remove() + +def inject_dropout_layers(policy, probability=0.5): + modules = list(policy.policy.nets['policy'].named_modules()) + #print(f"Total modules found: {len(modules)}") + + def dropout_hook(module, _, output): + #print("applying dropout") + return F.dropout(output, p=probability, training=True) # setting training=True forces dropout to happen regardless of whether the model is in train or evaluation mode + + hooks = [] + for name, module in modules: + if isinstance(module, nn.Linear): + hook = module.register_forward_hook(dropout_hook) + hooks.append(hook) + + return hooks + +def inject_dropout_layers_for_training(model, probability=0.5): + modules = list(model.nets['policy'].named_modules()) + #print(f"Total modules found: {len(modules)}") + + def dropout_hook(module, _, output): + #print("applying dropout") + return F.dropout(output, p=probability, training=True) # setting training=True forces dropout to happen regardless of whether the model is in train or evaluation mode + + hooks = [] + for name, module in modules: + if isinstance(module, nn.Linear): + hook = module.register_forward_hook(dropout_hook) + hooks.append(hook) + + return hooks + +def prepare_observations(obs_dict: dict): + # Prepare observations + obs = copy.deepcopy(obs_dict["policy"]) + sub_obs = copy.deepcopy(obs_dict["subtask_terms"]) + + for ob in obs: + obs[ob] = torch.squeeze(obs[ob]) + + for subob in sub_obs: + sub_obs[subob] = torch.squeeze(sub_obs[subob]) + + return obs, sub_obs + + +# device = TorchUtils.get_torch_device(try_to_use_cuda=True) + +# policy, _ = FileUtils.policy_from_checkpoint(ckpt_path=args_cli.checkpoint, device=device, verbose=True) + + +# obs_dict = {'policy': { +# 'joint_pos': torch.tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0.]], device=device), +# 'joint_vel': torch.tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0.]], device=device), +# 'object_position': torch.tensor([[0.4975, 0.0483, 0.0203]], device=device), +# 'target_object_position': torch.tensor([[ 0.4000, -0.3000, 0.0800, 1.0000, 0.0000, 0.0000, 0.0000]], device=device), +# 'actions': torch.tensor([[0., 0., 0., 0., 0., 0., 0.]], device=device), +# 'object_to_target': torch.tensor([False], device=device), +# 'eef_pos': torch.tensor([[ 0.3764, -0.0050, 0.0891]], device=device), +# 'eef_quat': torch.tensor([[ 0.6433, 0.0366, 0.7636, -0.0415]], device=device), +# 'gripper_pos': torch.tensor([[ 0.0400, -0.0400]], device=device) +# }, +# 'subtask_terms': { +# 'appr': torch.tensor([False], device=device), +# 'grasp': torch.tensor([False], device=device), +# 'lift': torch.tensor([False], device=device), +# 'appr_goal': torch.tensor([False], device=device) +# } +# } + +# inject_dropout_layers(policy=policy, probability=0.7) + +# policy.start_episode() +# obs, sub_obs = prepare_observations(obs_dict=obs_dict) + + +# start_time = time.time() +# results_dict = MC_dropout_uncertainty(policy=policy, obs=obs, niters=100) +# end_time = time.time() + +# print(results_dict) +# print(f"time taken: {end_time - start_time}") + +# ./isaaclab.sh -p scripts/imitation_learning/robomimic/play.py \ +# --device cuda --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --num_rollouts 50 \ +# --checkpoint /logs/docs/Models/bc/model1/Isaac-Stack-Cube-Franka-IK-Rel-v0/bc_rnn_low_dim_franka_stack/20250715152224/models/model_epoch_2000.pth + + +""" +train a model that: + +takes as input joint positions (so a combination of joint positions and their values). + +outputs whether that specific combination of joint positions is safe or not. + +for the training data use joint position input of the current policy and see if it resulted in task success or not. + +""" \ No newline at end of file diff --git a/scripts/imitation_learning/robomimic/play_ensemble.py b/scripts/imitation_learning/robomimic/play_ensemble.py new file mode 100644 index 000000000000..01b5d1f65f1d --- /dev/null +++ b/scripts/imitation_learning/robomimic/play_ensemble.py @@ -0,0 +1,968 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# run this script: +# ./isaaclab.sh -p scripts/imitation_learning/robomimic/play_ensemble.py --device cuda --task Dev-IK-Rel-v0 --num_rollouts 100 +# stack cube task: ./isaaclab.sh -p scripts/imitation_learning/robomimic/play.py --device cuda --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --num_rollouts 50 +"""Script to play and evaluate a trained policy from robomimic. + +This script loads a robomimic policy and plays it in an Isaac Lab environment. + +Args: + task: Name of the environment. + checkpoint: Path to the robomimic policy checkpoint. + horizon: If provided, override the step horizon of each rollout. + num_rollouts: If provided, override the number of rollouts. + seed: If provided, overeride the default random seed. + norm_factor_min: If provided, minimum value of the action space normalization factor. + norm_factor_max: If provided, maximum value of the action space normalization factor. +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Evaluate robomimic policy for Isaac Lab environment.") +parser.add_argument( + "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations." +) +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument("--checkpoint", type=str, default=None, help="Pytorch model checkpoint to load.") +parser.add_argument("--horizon", type=int, default=2000, help="Step horizon of each rollout.") +parser.add_argument("--num_rollouts", type=int, default=1, help="Number of rollouts.") +parser.add_argument("--seed", type=int, default=101, help="Random seed.") +parser.add_argument( + "--norm_factor_min", type=float, default=None, help="Optional: minimum value of the normalization factor." +) +parser.add_argument( + "--norm_factor_max", type=float, default=None, help="Optional: maximum value of the normalization factor." +) +parser.add_argument("--enable_pinocchio", default=False, action="store_true", help="Enable Pinocchio.") + +parser.add_argument("--model_name", type=str, default=None) + +group = parser.add_mutually_exclusive_group() +group.add_argument("--use_recovery", type=bool ,default= False, help="Enable recovery mechanism. By default recovery is enabled.") + +parser.add_argument("--ensemble_size", type=int, default=10) + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +if args_cli.enable_pinocchio: + # Import pinocchio before AppLauncher to force the use of the version installed by IsaacLab and not the one installed by Isaac Sim + # pinocchio is required by the Pink IK controllers and the GR1T2 retargeter + import pinocchio # noqa: F401 +import numpy as np +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import time +import copy +import random +import os +import gymnasium as gym +import torch +import math +from collections import deque + +import robomimic.utils.file_utils as FileUtils +import robomimic.utils.torch_utils as TorchUtils +from isaaclab_tasks.manager_based.manipulation.cube_lift.mdp.observations import get_joint_pos, object_grasped +#from chills.tasks.mdp.observations import get_joint_pos, object_grasped +if args_cli.enable_pinocchio: + import isaaclab_tasks.manager_based.manipulation.pick_place # noqa: F401 + +from isaaclab_tasks.utils import parse_env_cfg +#from isaaclab.utils.logging_helper import LoggingHelper, ErrorType, LogType +from isaaclab.utils.math import matrix_from_quat, convert_quat +from evaluation import ensemble_uncertainty +#import chills.tasks +#from chills.tasks.mdp import upright_tilt_deg + +def rollout(policy, env, success_term, horizon, device): + """Perform a single rollout of the policy in the environment. + + Args: + policy: The robomimicpolicy to play. + env: The environment to play in. + horizon: The step horizon of each rollout. + device: The device to run the policy on. + + Returns: + terminated: Whether the rollout terminated. + traj: The trajectory of the rollout. + """ + policy.start_episode() + obs_dict, _ = env.reset() + + #print(f"obs_dict type: {type(obs_dict)},\nobs_dict contents:\n {obs_dict}") + + traj = dict(actions=[], obs=[], next_obs=[], sub_obs=[], uncertainties=[]) + + for i in range(horizon): + # Prepare observations + obs = copy.deepcopy(obs_dict["policy"]) + sub_obs = copy.deepcopy(obs_dict["subtask_terms"]) + + for ob in obs: + obs[ob] = torch.squeeze(obs[ob]) + #print(f"found observation : {obs[ob]}") + + for subob in sub_obs: + sub_obs[subob] = torch.squeeze(sub_obs[subob]) + + # Check if environment image observations + if hasattr(env.cfg, "image_obs_list"): + # Process image observations for robomimic inference + for image_name in env.cfg.image_obs_list: + if image_name in obs_dict["policy"].keys(): + # Convert from chw uint8 to hwc normalized float + image = torch.squeeze(obs_dict["policy"][image_name]) + image = image.permute(2, 0, 1).clone().float() + image = image / 255.0 + image = image.clip(0.0, 1.0) + obs[image_name] = image + + traj["obs"].append(obs) + traj["sub_obs"].append(sub_obs) + + # Add dropout layers to the model and calculate uncertainty and remove at the end to not effect final action + + #hooks = inject_dropout_layers(policy=policy, probability=0.1) + # uncertainty_dict = MC_dropout_uncertainty(policy=policy, obs=obs, niters=15) + # traj['uncertainties'].append(uncertainty_dict['variance']) + #remove_dropout_layers(hooks) + + # Compute actions + actions = policy(obs) + + # Unnormalize actions + if args_cli.norm_factor_min is not None and args_cli.norm_factor_max is not None: + actions = ( + (actions + 1) * (args_cli.norm_factor_max - args_cli.norm_factor_min) + ) / 2 + args_cli.norm_factor_min + + actions = torch.from_numpy(actions).to(device=device).view(1, env.action_space.shape[1]) + + # Apply actions + obs_dict, _, terminated, truncated, _ = env.step(actions) + obs = obs_dict["policy"] + sub_obs = obs_dict["subtask_terms"] + + # Record trajectory + traj["actions"].append(actions.tolist()) + traj["next_obs"].append(obs) + + + # Check if rollout was successful + if bool(success_term.func(env, **success_term.params)[0]): + return True, traj + elif terminated or truncated: + return False, traj + + return False, traj +# tip over detection +def has_fallen(asset, threshold: float = -0.7, debug: bool = True) -> bool: + """ + Detect if an IsaacLab asset has fallen over by checking its up vector. + + Args: + asset: IsaacLab asset (e.g. robot, object) with .get_world_pose() + threshold: minimum z-component of the up vector (1.0 = perfectly upright). + Typical cutoff ~0.7 (≈45° tilt). + debug: if True, prints internal values for inspection. + + Returns: + True if fallen, False otherwise. + """ + quat = asset.data.body_link_quat_w # (pos, quat), usually torch.Tensors + # print("Beaker quat : ", quat) + # Ensure torch tensor, shape (4,) + if isinstance(quat, (list, tuple)): + quat = torch.tensor(quat, dtype=torch.float32) + quat = quat.view(-1, 4) # shape (1,4) + + # Convert quaternion to wxyz ordering + quat_wxyz = convert_quat(quat, to="wxyz") + + # Rotation matrix (1,3,3) + R = matrix_from_quat(quat_wxyz) + + # World up vector is the third column of rotation matrix + up = R[0, :, 2] # shape (3,) + uprightness = float(up[2]) + + if debug: + print(f"[has_fallen] quat={quat_wxyz.cpu().numpy().round(4)}") + print(f"[has_fallen] up={up.cpu().numpy().round(4)} uprightness={uprightness:.3f}") + + return uprightness > threshold + +def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, use_recovery=True, rollout_num=0): + """Perform a single rollout of the policy in the environment. + + Args: + policy: The robomimicpolicy to play. + env: The environment to play in. + horizon: The step horizon of each rollout. + device: The device to run the policy on. + + Returns: + terminated: Whether the rollout terminated. + traj: The trajectory of the rollout. + """ + print("running rollout number : ", rollout_num) + for policy in ensemble: + policy.start_episode() + obs_dict, _ = env.reset() + + #print(f"obs_dict type: {type(obs_dict)},\nobs_dict contents:\n {obs_dict}") + + traj = dict(actions=[], obs=[], next_obs=[], sub_obs=[], + uncertainties=[], min_actions=[], max_actions=[], + time_taken=[], joint_pos=[], recovery=[]) + + joints_to_check = [0, 1, 2, 3, 4, 5, 6] + unsafe_windows_detected = {joint_num: 0 for joint_num in joints_to_check} + windows = {joint_num: deque(maxlen=parameters[joint_num]['window_size']) for joint_num in joints_to_check} + certain_timestep = 50 + certain_joint_positions = [] + + ### SET USE RECOVERY TO FALSE #### + use_recovery=False + recovery_activated_during_rollout = 0 +#########SET TO FALSE ####### + recovery_mode = False + print("rollout recovery enabled ? : ", use_recovery) + recovery_duration = 100 + recovery_cooldown_duration = 100 # timesteps + recovery_cooldown_timer = recovery_cooldown_duration + recovery_cooldown_active = False + recovery = [] + for i in range(horizon): + # Prepare observations + if bool(success_term.func(env, **success_term.params)[0]): + return True, traj, recovery_activated_during_rollout + obs = copy.deepcopy(obs_dict["policy"]) + sub_obs = copy.deepcopy(obs_dict["subtask_terms"]) + object = env.unwrapped.scene["object"] + + # is_tipped = has_fallen(object, -0.95, True) + for ob in obs: + obs[ob] = torch.squeeze(obs[ob]) + #print(f"found observation : {obs[ob]}") + + for subob in sub_obs: + sub_obs[subob] = torch.squeeze(sub_obs[subob]) + + # Check if environment image observations + if hasattr(env.cfg, "image_obs_list"): + # Process image observations for robomimic inference + for image_name in env.cfg.image_obs_list: + if image_name in obs_dict["policy"].keys(): + # Convert from chw uint8 to hwc normalized float + image = torch.squeeze(obs_dict["policy"][image_name]) + image = image.permute(2, 0, 1).clone().float() + image = image / 255.0 + image = image.clip(0.0, 1.0) + obs[image_name] = image + + traj["obs"].append(obs) + traj["sub_obs"].append(sub_obs) + object=env.unwrapped.scene['object'] + #tilt_deg = upright_tilt_deg(object.data.root_quat_w, object.data.default_root_state[:, 3:7]) + #print("Tilt deg : ", tilt_deg.item()) + if recovery_mode: + print(f"Recovery Mode Started at Timestep {i}") + robot = env.unwrapped.scene["robot"] + tmp_pos = get_joint_pos(env) + # print("Temp, gripper a pos : ", tmp_pos[0][7]) + #print("Temp, gripper b pos : ", tmp_pos[0][8]) + safe_pos = None + if object_grasped(env): + # mid safe place - should help with the task of moving the beaker to the target + safe_pos = torch.tensor([[0.2932, 0.2640, -0.3573, -2.6099, -2.8971, 2.0274, 0.8245, tmp_pos[0][7], tmp_pos[0][8]]]).to(device) + else: + # origin + safe_pos = torch.tensor([[ 0.405,0.35,-0.22,-3,-2.85,math.pi/2,0.9, tmp_pos[0][7],tmp_pos[0][8]]]).to(device)# safe_pos = certain_joint_positions[-certain_timestep] + position_errors = deque(maxlen=50) + rec_i = 0 + + current = get_joint_pos(env) + target = safe_pos + steps = 200 # increase for slower motion + + for alpha in np.linspace(0, 1, steps): + recovery.append(True) + intermediate = (1 - alpha) * current + alpha * target + robot.set_joint_position_target(intermediate) + env.scene.write_data_to_sim() + env.sim.step() + if bool(success_term.func(env, **success_term.params)[0]): + return True, traj, recovery_activated_during_rollout + + # while rec_i < recovery_duration: + # # print("In recovery") + # recovery.append(True) + # robot.set_joint_position_target(safe_pos) # expects an absolute joint position + # env.scene.write_data_to_sim() + # env.sim.step() + + # # Check if robot reached the safe position within a small tolerance + # new_joint_positions = get_joint_pos(env) + # target_joint_positions = safe_pos + + # position_error = torch.abs(new_joint_positions - target_joint_positions) + # position_errors.append(position_error) + # tolerance = 1e-2 + + # # check if the positon/position error has converged to the safe position (it wont reach it exactly). + # # it also jumps to it straight away but if you stop straight away you get weird jerky motion and it doesnt actually reach the safe position + # # check every 50 iterations + # if rec_i % 50 == 0: + # reached_safe_pos_early = False + # if position_errors.maxlen == len(position_errors): + # for pe in position_errors: + # if torch.all(torch.abs(pe - position_error) < tolerance): + # reached_safe_pos_early = True + # if reached_safe_pos_early: + # print(f"Reached safe position early.") + # break + # # else extend the recovery duration + # if rec_i == recovery_duration - 1: + # print("Extending Recovery Duration") + # recovery_duration += 50 + + # rec_i += 1 + + print(f"Recovery Mode Ended") + print("Recovery Cooldown Activated") + recovery_mode = False + recovery_cooldown_active = True + obs_dict, _, terminated, truncated, _ = env.step(torch.tensor([0 for _ in range(7)]).to(device).view(1, env.action_space.shape[1])) + obs = copy.deepcopy(obs_dict["policy"]) + for ob in obs: + obs[ob] = torch.squeeze(obs[ob]) + ensemble_uncertainty(ensemble, obs) + else: + # Calculate uncertainty using the ensemble + recovery.append(False) + if bool(success_term.func(env, **success_term.params)[0]): + return True, traj, recovery_activated_during_rollout + ## detect if we have tipped the object + + + metrics = ensemble_uncertainty(ensemble, obs) + # Get the std and mean action + uncertainty = metrics['variance'] + actions = metrics['mean'] + + # Add the uncertainty to all joint windows + for joint_num, unc in enumerate(uncertainty): + windows[joint_num].append(unc) + + for joint_num in joints_to_check: + unc_threshold = parameters[joint_num]['unc_threshold'] + peaks = sum(1 for curr_unc in windows[joint_num] if curr_unc > unc_threshold) + if peaks > parameters[joint_num]['max_peaks']: + unsafe_windows_detected[joint_num] += 1 + + + for joint_num in joints_to_check: + ## less worried after the object grasped + if unsafe_windows_detected[joint_num] > parameters[joint_num]['max_uncertain_windows'] and not recovery_cooldown_active: + print(f"Joint {joint_num} is uncertain at timestep {i}, uncertainty is {uncertainty[joint_num]}") + unsafe_windows_detected[joint_num] = 0 + if use_recovery: + recovery_mode = True + recovery_activated_during_rollout += 1 + # if object_grasped(env): + + # else : + # ## more strict when grasping + # if (unsafe_windows_detected[joint_num] > (parameters[joint_num]['max_uncertain_windows']-1)) and not recovery_cooldown_active: + # print(f"Object not grapsed, using more strict uncertainty Joint {joint_num} is uncertain at timestep {i}, uncertainty is {uncertainty[joint_num]}") + # unsafe_windows_detected[joint_num] = 0 + # if obs_dict['subtask_terms']['appr_goal'].item(): + # # then we are placing the object , do not recover + # print("not activating recovery, object at goal") + # elif use_recovery: + # recovery_mode = True + # recovery_activated_during_rollout += 1 + + if use_recovery and not recovery_cooldown_active: + # print("not holding object, check if not hit it") + # print("observed tilt " , obs_dict['policy']['object_tilt']) + # if obs_dict['policy']['object_tilt'].item() > 5.0: + # print("Ucertainties when tilt : ",uncertainty) + # print("Object tilted, activating no tilt recovery ") + # print("observed tilt " , obs_dict['policy']['object_tilt']) + recovery_mode =True + recovery_activated_during_rollout+=1 + + if recovery_cooldown_active: + if object_grasped(env): + # normal recovery if already holding + recovery_cooldown_timer-=1 + else: + # shorten the cooldown for grasping stage + + recovery_cooldown_timer -= 1 + + if recovery_cooldown_timer <= 0: + recovery_cooldown_active = False + recovery_cooldown_timer = recovery_cooldown_duration + print("Recovery Cooldown Ended") + + if not recovery_mode: + current_absolute_joints = get_joint_pos(env) + certain_joint_positions.append(current_absolute_joints) + + + # Unnormalize actions + if args_cli.norm_factor_min is not None and args_cli.norm_factor_max is not None: + actions = ( + (actions + 1) * (args_cli.norm_factor_max - args_cli.norm_factor_min) + ) / 2 + args_cli.norm_factor_min + + actions = actions.to(device=device).view(1, env.action_space.shape[1]) + # print(f"------------------------------") + # print(f"Policy action : {actions.tolist()}") + + # Apply actions + obs_dict, _, terminated, truncated, _ = env.step(actions) + obs = obs_dict["policy"] + sub_obs = obs_dict["subtask_terms"] + # print(f"Obs change : {obs}") + # Record trajectory + traj["actions"].append(actions.tolist()) + traj["next_obs"].append(obs) + traj['uncertainties'].append(uncertainty) + traj['max_actions'].append(metrics['max']) + traj['min_actions'].append(metrics['min']) + traj['time_taken'].append(metrics['time_taken']) + traj['joint_pos'].append(obs['abs_joint_pos']) + traj['recovery'].append(recovery) + + + # Check if rollout was successful + if bool(success_term.func(env, **success_term.params)[0]): + return True, traj, recovery_activated_during_rollout + elif terminated or truncated: + return False, traj, recovery_activated_during_rollout + #print("REcovery : ", traj["recovery"]) + return False, traj, recovery_activated_during_rollout + + +def safety(): + print(f"I'm Uncertain. {random.randint(0, 100)}") + return True + +def rollout_transformer(policy, env, success_term, horizon, device): + """Perform a single rollout of the policy in the environment, supporting sequence-based models.""" + policy.start_episode() + obs_dict, _ = env.reset() + traj = dict(actions=[], obs=[], next_obs=[], sub_obs=[], uncertainties=[]) + + context_length = getattr(policy, "n_obs_steps", 1) # works for transformer or rnn models + # print(f"Policy's expected sequence length (policy.n_obs_steps): {getattr(policy, 'n_obs_steps', 1)}") + + obs_seq = [] # list of previous obs for context window + + # Initial obs_dict from env.reset() + # print("Initial obs_dict shapes from env.reset():") + # for k, v in obs_dict["policy"].items(): + # print(f" policy['{k}']: shape={v.shape}, ndim={v.ndim}") + + # try: + # if hasattr(policy.policy, 'encoder') and hasattr(policy.policy.encoder, 'input_obs_group_shapes'): + # print("Policy's internal expected input_obs_group_shapes:") + # for group, keys in policy.policy.encoder.input_obs_group_shapes.items(): + # print(f" Group '{group}':") + # for k, shape in keys.items(): + # print(f" Key '{k}': shape={shape}, len(shape)={len(shape)}") + # else: + # print("Could not find policy.policy.encoder.input_obs_group_shapes.") + # except Exception as e: + # print(f"Error accessing policy.policy.encoder.input_obs_group_shapes: {e}") + + + for i in range(horizon): + # Prepare current observations from env for policy input + current_policy_obs = {} + for k, v in obs_dict["policy"].items(): + # Ensure low-dim observations are 1D (D,) + # If env returns (1, D), squeeze the batch dimension + if v.ndim > 1 and v.shape[0] == 1: + processed_v = v.squeeze(0).to(device) # Apply squeeze + # print(f" DEBUG: Squeezed '{k}' from {v.shape} to {processed_v.shape}") # Add debug print + else: + processed_v = v.to(device) # No squeeze needed, assume (D,) + current_policy_obs[k] = processed_v + + # # AFTER processing, print the shapes in current_policy_obs + # if i == 0: # Only print for the first step to avoid spamming + # print("current_policy_obs shapes AFTER squeezing (if applicable):") + # for k, v in current_policy_obs.items(): + # print(f" '{k}': shape={v.shape}, ndim={v.ndim}") + + + # Handle image observations specifically (if any) + if hasattr(env.cfg, "image_obs_list"): + for image_name in env.cfg.image_obs_list: + if image_name in obs_dict["policy"].keys(): + image = obs_dict["policy"][image_name].to(device) + # Assuming image comes as (H, W, C) and needs to be (C, H, W) + # If it's already (1, H, W, C), squeeze the batch dim first. + if image.ndim == 4 and image.shape[0] == 1: + image = image.squeeze(0) # Remove initial batch if present + + # Permute and normalize after ensuring no batch dim + image = image.permute(2, 0, 1).clone().float() # (C, H, W) + image = image / 255.0 + image = image.clip(0.0, 1.0) + current_policy_obs[image_name] = image + + + # Add subtask terms for logging/analysis, but they are not fed to the policy + # as per your trace (only "policy" observations are listed in the policy's encoder). + # You had prints for these before, assuming they are okay. + + # Append to context buffer. obs_seq should hold items with shape (D,) or (C, H, W) + traj["obs"].append(current_policy_obs) # Store the current, processed observation + obs_seq.append(current_policy_obs) # This is what forms the sequence input + + if len(obs_seq) > context_length: + obs_seq = obs_seq[-context_length:] + + # Pad if not enough context + if len(obs_seq) < context_length: + padding_obs = {} + for k, v in obs_seq[0].items(): # Use shape of first element in obs_seq for padding + # If obs_seq[0][k] is (D,) then zeros_like creates (D,) + # If obs_seq[0][k] is (C,H,W) then zeros_like creates (C,H,W) + padding_obs[k] = torch.zeros_like(v) + obs_seq = [padding_obs] * (context_length - len(obs_seq)) + obs_seq + + # Convert context list to batched sequence dict + seq_input = {} + for key in obs_seq[0]: + # Each step[key] here should be (D,) or (C, H, W) + # torch.stack will create (context_length, D) or (context_length, C, H, W) + # unsqueeze(0) will add the batch dimension: (1, context_length, D) or (1, context_length, C, H, W) + seq_input[key] = torch.stack([step[key] for step in obs_seq], dim=0).to(device=device) + #seq_input[key] = torch.stack([step[key] for step in obs_seq], dim=0).unsqueeze(0).to(device=device) + + # Print the final seq_input shapes just before policy call + # if i == 0: # Only print for the first step + # print("seq_input shapes for policy just before call:") + # for k, v in seq_input.items(): + # print(f" '{k}': shape={v.shape}, ndim={v.ndim}") + + # if i == 0: # Only print for the first step + # print("seq_input shapes for policy just before policy(seq_input) call (LAST CHECK):") + # for k, v in seq_input.items(): + # print(f" '{k}': shape={v.shape}, ndim={v.ndim}") + + # calculate uncertainty + # uncertainty_dict = mc_dropout_uncertainty_eval(policy=policy, obs=seq_input, niters=15) + # traj['uncertainties'].append(uncertainty_dict['variance']) + + # Compute action from sequence + actions = policy(seq_input) + + #print(f"DEBUG (Pre-Unnorm): Policy output 'actions' shape: {actions.shape}, ndim: {actions.ndim}, type: {type(actions)}") + + # Unnormalize actions + if args_cli.norm_factor_min is not None and args_cli.norm_factor_max is not None: + actions = ( + (actions + 1) * (args_cli.norm_factor_max - args_cli.norm_factor_min) + ) / 2 + args_cli.norm_factor_min + #print(f"DEBUG (Post-Unnorm): Policy output 'actions' shape: {actions.shape}, ndim: {actions.ndim}, type: {type(actions)}") + # Convert policy output (torch.Tensor) to numpy array for env.step() + # Assume actions comes as (1, Action_Dim) from policy, squeeze to (Action_Dim,) + if isinstance(actions, torch.Tensor): + # If it's a tensor, convert it to numpy, ensuring it's 1D (7,) + # We already know policy returns (7,) so no squeeze is needed *here*. + actions = actions.cpu().numpy() + + actions_tensor = torch.from_numpy(actions).to(device=device).float() + actions_tensor = actions_tensor.unsqueeze(0) + # Apply actions + #obs_dict, _, terminated, truncated, _ = env.step(actions) + obs_dict, _, terminated, truncated, _ = env.step(actions_tensor) + # Record trajectory - traj["next_obs"] should append the raw observation dictionary from env.step(). + traj["actions"].append(actions.tolist()) + traj["next_obs"].append(obs_dict["policy"]) + + + # Check if rollout was successful + if bool(success_term.func(env, **success_term.params)[0]): + return True, traj + elif terminated or truncated: + return False, traj + + return False, traj + +# ./isaaclab.sh -p scripts/imitation_learning/robomimic/play.py \ +# --device cuda --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --num_rollouts 50 \ +# --checkpoint /logs/docs/Models/bc/model1/Isaac-Stack-Cube-Franka-IK-Rel-v0/bc_rnn_low_dim_franka_stack/20250715152224/models/model_epoch_2000.pth + + +# ./isaaclab.sh -p scripts/imitation_learning/robomimic/play.py --device cuda --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --num_rollouts 10 --checkpoint logs/docs/Models/bc/model2/Isaac-Stack-Cube-Franka-IK-Rel-v0/bc_rnn_low_dim_franka_stack/20250715152538/models/model_epoch_2000.pth --headless + + + +# ./isaaclab.sh -p scripts/imitation_learning/robomimic/train.py \ +# --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --algo bc \ +# --dataset ./docs/training_data/generated_dataset_split.hdf5 --logdir ./logs/docs/Models/bc/model8/ + + + +def load_ensemble(device, ensemble_path): + with open(ensemble_path, 'r') as file: + ensemble_paths = [path.strip() for path in file.readlines()] + ensemble = [] + for path in ensemble_paths: + policy, _ = FileUtils.policy_from_checkpoint(ckpt_path=path, device=device, verbose=True) + ensemble.append(policy) + print(f"[DEBUG] loaded {len(ensemble)} policies") + return ensemble + +def clear_files(*paths : str): + if len(paths) < 1: + raise ValueError("At least one path is required.") + if os.path.exists(paths[0]): + #overwrite = input(f"You are about to overwrite rollout data at: {paths}\nContinue (y/n):") + overwrite = "y" + match overwrite.lower(): + case 'y' | 'Y': + for path in paths: + with open(path, 'w') as file: + pass + case _: + exit() + + + +def main(): + """Run a trained policy from robomimic with Isaac Lab environment.""" + # parse configuration + env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=1, use_fabric=not args_cli.disable_fabric) + + # Set observations to dictionary mode for Robomimic + env_cfg.observations.policy.concatenate_terms = False + + #create a log handler + # loghelper = LoggingHelper() + + # Set termination conditions + env_cfg.terminations.time_out = None + + # Disable recorder + env_cfg.recorders = None + + # Extract success checking function + success_term = env_cfg.terminations.success + env_cfg.terminations.success = None + + # Create environment + env = gym.make(args_cli.task, cfg=env_cfg).unwrapped + + # Set seed + torch.manual_seed(args_cli.seed) + env.seed(args_cli.seed) + + # Acquire device + device = TorchUtils.get_torch_device(try_to_use_cuda=True) + + # load the stack cube ensemble + # stack_cube_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/stack_cube_ensemble.txt') + + pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/ensembles.txt') + # pick_place_ensemble_30 = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/pick_place_ensemble_30_paths.txt') + + + parameters = { + 'stack_cube': { + 6: { + 'unc_threshold': 0.05, + 'max_peaks': 15, + 'window_size': 30, + 'max_uncertain_windows': 3 + }, + 5: { + 'unc_threshold': 0.06, + 'max_peaks': 2, + 'window_size': 10, + 'max_uncertain_windows': 3 + }, + + 4: { + 'unc_threshold': 0.04, + 'max_peaks': 2, + 'window_size': 10, + 'max_uncertain_windows': 3 + }, + 3: { + 'unc_threshold': 0.04, + 'max_peaks': 2, + 'window_size': 10, + 'max_uncertain_windows': 3 + }, + 2: { + 'unc_threshold': 0.04, + 'max_peaks': 2, + 'window_size': 10, + 'max_uncertain_windows': 3 + }, + 1: { + 'unc_threshold': 0.04, + 'max_peaks': 2, + 'window_size': 10, + 'max_uncertain_windows': 3 + }, + 0: { + 'unc_threshold': 0.04, + 'max_peaks': 2, + 'window_size': 10, + 'max_uncertain_windows': 3 + } + }, + + 'pick_place': { + 6: { + 'unc_threshold': 0.05, + 'max_peaks': 15, + 'window_size': 30, + 'max_uncertain_windows': 3 + }, + 5: { + 'unc_threshold': 0.02, + 'max_peaks': 2, + 'window_size': 10, + 'max_uncertain_windows': 1 + }, + + 4: { + 'unc_threshold': 0.03, + 'max_peaks': 2, + 'window_size': 10, + 'max_uncertain_windows': 3 + }, + 3: { + 'unc_threshold': 0.03, + 'max_peaks': 2, + 'window_size': 10, + 'max_uncertain_windows': 3 + }, + 2: { + 'unc_threshold': 0.02, + 'max_peaks': 2, + 'window_size': 10, + 'max_uncertain_windows': 3 + }, + 1: { + 'unc_threshold': 0.03, + 'max_peaks': 2, + 'window_size': 10, + 'max_uncertain_windows': 3 + }, + 0: { + 'unc_threshold': 0.03, + 'max_peaks': 2, + 'window_size': 10, + 'max_uncertain_windows': 3 + } + + }, + 'stack_beaker': { + 6: { + 'unc_threshold': 0.05, + 'max_peaks': 15, + 'window_size': 30, + 'max_uncertain_windows': 3 + }, + 5: { + 'unc_threshold': 0.02, + 'max_peaks': 2, + 'window_size': 10, + 'max_uncertain_windows': 3 + }, + + 4: { + 'unc_threshold': 0.03, + 'max_peaks': 2, + 'window_size': 10, + 'max_uncertain_windows': 3 + }, + 3: { + 'unc_threshold': 0.03, + 'max_peaks': 5, + 'window_size': 10, + 'max_uncertain_windows': 3 + }, + 2: { + 'unc_threshold': 0.02, + 'max_peaks': 2, + 'window_size': 10, + 'max_uncertain_windows': 3 + }, + 1: { + 'unc_threshold': 0.03, + 'max_peaks': 5, + 'window_size': 10, + 'max_uncertain_windows': 3 + }, + 0: { + 'unc_threshold': 0.03, + 'max_peaks': 2, + 'window_size': 10, + 'max_uncertain_windows': 3 + }, + } + } + + model_arch = "BC_RNN_GMM" + task = "pick_place" # stack_cube or pick_place + model_name = f"ensemble" + number = 'no_recovery_video' + + results_path = f"docs/training_data/{task}/uncertainty_rollout_{task}/{model_name}/run_{number}" + + uncertainties_path = f"{results_path}/uncertainties{number}.txt" + actions_path = f"{results_path}/actions{number}.txt" + min_actions_path = f"{results_path}/min_actions{number}.txt" + max_actions_path = f"{results_path}/max_actions{number}.txt" + time_taken_path = f"{results_path}/time_taken{number}.txt" + recovery_activated_during_rollout_path = f"{results_path}/recovery_activated_during_rollout.txt" + rollout_uncert_path = f"{uncertainties_path}/rollout_uncerts" + rollout_log_path = f"{uncertainties_path[:len(uncertainties_path)-4]}_rollout_log.txt" + + rollout_log_path = f"{uncertainties_path[:len(uncertainties_path)-4]}_rollout_log.txt" # remove the '.txt' and add rollout_log.txt + + uncertainty_results_path = f"{results_path}/uncertainty_plots" + trajectory_results_path = f"{results_path}/trajectory_plots" + os.makedirs(uncertainty_results_path, exist_ok=True) + os.makedirs(trajectory_results_path, exist_ok=True) + + # try: + # os.remove(rollout_log_path) + # except FileNotFoundError as filenotfounderror: + # pass + + # clear files + clear_files( + uncertainties_path, actions_path, + min_actions_path, max_actions_path, + time_taken_path, recovery_activated_during_rollout_path + ) + + #clear uncertainties file + with open(uncertainties_path, 'w') as file: + pass + with open(actions_path, 'w') as file: + pass + with open(min_actions_path, 'w') as file: + pass + with open(max_actions_path, 'w') as file: + pass + with open(time_taken_path, 'w') as file: + pass + with open(recovery_activated_during_rollout_path, 'w') as file: + pass + # clear rollout logger file + #with open(loghelper.namefile, 'w') as file: + # pass + + # Run policy + results = [] + for trial in range(args_cli.num_rollouts): + print(f"[INFO] Starting trial {trial}") + + terminated, traj, recovery_activated_during_rollout = rollout_ensemble(pick_place_ensemble[:args_cli.ensemble_size], env, success_term, args_cli.horizon, device, parameters['pick_place'], use_recovery=args_cli.use_recovery, rollout_num=trial) + # save the uncertainties + print("Finished rollout, recovery needed : ", recovery_activated_during_rollout) + #print("actions shape : ", traj['actions']) + for j in range(7): + with open(f"docs/rollouts/rollout_logging_no_recovery_joint{j}.txt", "a") as file: + #file.write(f"Logs for joint {j}\n") + for i, var in enumerate(traj['actions']): + # get all the info per joint + act = str(traj['actions'][i][0][j]) + max = str(traj['max_actions'][i].values.tolist()[j]) + min = str(traj['min_actions'][i].values.tolist()[j]) + uncert = str(traj['uncertainties'][i].tolist()[j]) + #print("joint pos : ", str(traj['joint_pos'][i][0].tolist()[j])) + joint_pos = str(traj['joint_pos'][i][0].tolist()[j]) + recovery = str(traj['recovery'][0][i]) + # print("writing recovery : ", recovery) + line = f"{i} : {act} : {max} : {min} : {uncert} : {joint_pos}: {recovery}: {terminated}\n" + file.write(line) + + + for j in range(7): + joint_uncert = [t[j].item() for t in traj['uncertainties']] + with open(f"docs/rollouts/rollout_uncerts_joint{j}.txt", "a") as f: + line = ",".join(["{:.10f}".format(v) for v in joint_uncert]) + f.write(line + f" : {terminated} \n") + + with open(uncertainties_path, 'a') as file: + for i, var in enumerate(traj['uncertainties']): + line = " ".join([str(v.item()) for v in var]) + f" {terminated}\n" + file.write(f"{str(i)} {line}") + # save the actions + with open(actions_path, 'a') as file: + for i, var in enumerate(traj['actions']): + line = " ".join([str(v) for v in var[0]]) + f" {terminated}\n" + file.write(f"{str(i)} {line}") + # save the max actions + with open(max_actions_path, 'a') as file: + for i, var in enumerate(traj['max_actions']): + line = " ".join([str(v.item()) for v in var[0]]) + f" {terminated}\n" + file.write(f"{str(i)} {line}") + # save the min actions + with open(min_actions_path, 'a') as file: + for i, var in enumerate(traj['min_actions']): + line = " ".join([str(v.item()) for v in var[0]]) + f" {terminated}\n" + file.write(f"{str(i)} {line}") + # save time taken + with open(time_taken_path, 'a') as file: + for i, time_taken in enumerate(traj['time_taken']): + line = f"{str(time_taken)} {terminated}\n" + file.write(f"{str(i)} {line}") + # save recovery activated + with open("recovery_actions.txt", 'a') as file: + file.write(f"{trial} {recovery_activated_during_rollout} {terminated}\n") + + + results.append(terminated) + print(f"[INFO] Trial {trial}: {terminated}\n") + #loghelper.stopEpoch(trial) + + print(f"\nSuccessful trials: {results.count(True)}, out of {len(results)} trials") + print(f"Success rate: {results.count(True) / len(results)}") + print(f"Trial Results: {results}\n") + + + env.close() + + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() \ No newline at end of file diff --git a/scripts/imitation_learning/robomimic/train_ensemble.py b/scripts/imitation_learning/robomimic/train_ensemble.py new file mode 100644 index 000000000000..67f6ba3160fc --- /dev/null +++ b/scripts/imitation_learning/robomimic/train_ensemble.py @@ -0,0 +1,532 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# MIT License +# +# Copyright (c) 2021 Stanford Vision and Learning Lab +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +""" +The main entry point for training policies from pre-collected data. + +This script loads dataset(s), creates a model based on the algorithm specified, +and trains the model. It supports training on various environments with multiple +algorithms from robomimic. + +Args: + algo: Name of the algorithm to run. + task: Name of the environment. + name: If provided, override the experiment name defined in the config. + dataset: If provided, override the dataset path defined in the config. + log_dir: Directory to save logs. + normalize_training_actions: Whether to normalize actions in the training data. + +This file has been modified from the original robomimic version to integrate with IsaacLab. +""" + +"""Launch Isaac Sim Simulator first.""" + +from isaaclab.app import AppLauncher + +# launch omniverse app +app_launcher = AppLauncher(headless=True) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +# Standard library imports +import argparse + +# Third-party imports +import gymnasium as gym +import h5py +import json +import numpy as np +import os +import shutil +import sys +import time +import torch +import traceback +import subprocess +import wandb + +from collections import OrderedDict +from torch.utils.data import DataLoader +from torch.utils.data import Subset + +import psutil + +# Robomimic imports +import robomimic.utils.env_utils as EnvUtils +import robomimic.utils.file_utils as FileUtils +import robomimic.utils.obs_utils as ObsUtils +import robomimic.utils.torch_utils as TorchUtils +import robomimic.utils.train_utils as TrainUtils +from robomimic.algo import algo_factory +from robomimic.config import Config, config_factory +from robomimic.utils.log_utils import DataLogger, PrintLogger + +# Isaac Lab imports (needed so that environment is registered) +import isaaclab_tasks # noqa: F401 +import isaaclab_tasks.manager_based.manipulation.pick_place # noqa: F401 + +from evaluation import inject_dropout_layers_for_training, remove_dropout_layers + +def normalize_hdf5_actions(config: Config, log_dir: str) -> str: + """Normalizes actions in hdf5 dataset to [-1, 1] range. + + Args: + config: The configuration object containing dataset path. + log_dir: Directory to save normalization parameters. + + Returns: + Path to the normalized dataset. + """ + base, ext = os.path.splitext(config.train.data) + normalized_path = base + "_normalized" + ext + + # Copy the original dataset + print(f"Creating normalized dataset at {normalized_path}") + shutil.copyfile(config.train.data, normalized_path) + + # Open the new dataset and normalize the actions + with h5py.File(normalized_path, "r+") as f: + dataset_paths = [f"/data/demo_{str(i)}/actions" for i in range(len(f["data"].keys()))] + + # Compute the min and max of the dataset + dataset = np.array(f[dataset_paths[0]]).flatten() + for i, path in enumerate(dataset_paths): + if i != 0: + data = np.array(f[path]).flatten() + dataset = np.append(dataset, data) + + max = np.max(dataset) + min = np.min(dataset) + + # Normalize the actions + for i, path in enumerate(dataset_paths): + data = np.array(f[path]) + normalized_data = 2 * ((data - min) / (max - min)) - 1 # Scale to [-1, 1] range + del f[path] + f[path] = normalized_data + + # Save the min and max values to log directory + with open(os.path.join(log_dir, "normalization_params.txt"), "w") as f: + f.write(f"min: {min}\n") + f.write(f"max: {max}\n") + + return normalized_path + + +# + +def train(config: Config, device: str, log_dirs: list[str], ckpt_dirs: list[str], video_dirs: list[str], use_config_seed: bool = True, ensemble_size: int = 1): + """Train an ensemble of models using the algorithm specified in config. If ensemble_size is not specified, a single model will be trained. + + Args: + config: Configuration object. + device: PyTorch device to use for training. + log_dirs: List of directories to save logs for each network in the ensemble. + ckpt_dirs: List of directories to save checkpoints for each network in the ensemble. + video_dirs: List of directories to save videos for each network in the ensemble. + """ + # first set seeds + if use_config_seed: + np.random.seed(config.train.seed) + torch.manual_seed(config.train.seed) + + + print("\n============= New Training Run with Config =============") + print(config) + print("") + + + # read config to set up metadata for observation modalities (e.g. detecting rgb observations) + ObsUtils.initialize_obs_utils_with_config(config) + + # make sure the dataset exists + dataset_path = os.path.expanduser(config.train.data) + if not os.path.exists(dataset_path): + raise FileNotFoundError(f"Dataset at provided path {dataset_path} not found!") + + # load basic metadata from training file + print("\n============= Loaded Environment Metadata =============") + env_meta = FileUtils.get_env_metadata_from_dataset(dataset_path=config.train.data) + shape_meta = FileUtils.get_shape_metadata_from_dataset( + dataset_path=config.train.data, all_obs_keys=config.all_obs_keys, verbose=True + ) + + if config.experiment.env is not None: + env_meta["env_name"] = config.experiment.env + print("=" * 30 + "\n" + "Replacing Env to {}\n".format(env_meta["env_name"]) + "=" * 30) + + # create environment + envs = OrderedDict() + if config.experiment.rollout.enabled: + # create environments for validation runs + env_names = [env_meta["env_name"]] + + if config.experiment.additional_envs is not None: + for name in config.experiment.additional_envs: + env_names.append(name) + + for env_name in env_names: + env = EnvUtils.create_env_from_metadata( + env_meta=env_meta, + env_name=env_name, + render=False, + render_offscreen=config.experiment.render_video, + use_image_obs=shape_meta["use_images"], + ) + envs[env.name] = env + print(envs[env.name]) + + print("") + + + # load training data + trainset, validset = TrainUtils.load_data_for_training(config, obs_keys=shape_meta["all_obs_keys"]) + + # reduce size of the dataset + trainset = keep_data_percentage(trainset, args.dataset_percentage) + + #train_sampler = trainset.get_dataset_sampler() + if isinstance(trainset, Subset): + train_sampler = trainset.dataset.get_dataset_sampler() + else: + train_sampler = trainset.get_dataset_sampler() + + + print("\n============= Training Dataset =============") + print(trainset) + print("") + + # maybe retrieve statistics for normalizing observations + obs_normalization_stats = None + if config.train.hdf5_normalize_obs: + obs_normalization_stats = trainset.get_obs_normalization_stats() + + # initialize data loaders + train_loader = DataLoader( + dataset=trainset, + sampler=train_sampler, + batch_size=config.train.batch_size, + shuffle=(train_sampler is None), + num_workers=config.train.num_data_workers, + drop_last=True, + ) + + if config.experiment.validate: + # cap num workers for validation dataset at 1 + num_workers = min(config.train.num_data_workers, 1) + valid_sampler = validset.get_dataset_sampler() + valid_loader = DataLoader( + dataset=validset, + sampler=valid_sampler, + batch_size=config.train.batch_size, + shuffle=(valid_sampler is None), + num_workers=num_workers, + drop_last=True, + ) + else: + valid_loader = None + + # main training loop + #best_valid_loss = None + last_ckpt_time = time.time() + + # number of learning steps per epoch (defaults to a full dataset pass) + train_num_steps = config.experiment.epoch_every_n_steps + valid_num_steps = config.experiment.validation_epoch_every_n_steps + + #config.algo.transformer.num_layers = 1 + #config.algo.transformer.num_heads = 1 + for model_num in range(ensemble_size): + # setup a wand run + wand_experiment_name = f"{config.experiment.name}-model{model_num}" + run=wandb.init( + project=config.experiment.logging.wandb_proj_name, + name=wand_experiment_name + ) + #run.define_metric("train reward", step_metric="episode") + # run.define_metric("episode error", step_metric="episode") + # reset best valid loss + best_valid_loss = None + + log_dir = log_dirs[model_num] + ckpt_dir = ckpt_dirs[model_num] + video_dir = video_dirs[model_num] + + # setup logger stuff + # logger = Logger(os.path.join(log_dir, "log.txt")) + # sys.stdout = logger + # sys.stderr = logger + data_logger = DataLogger(log_dir, config=config, log_tb=config.experiment.logging.log_tb, log_wandb=config.experiment.logging.log_wandb) + with open(os.path.join(log_dir, "..", "config.json"), "w") as outfile: + json.dump(config, outfile, indent=4) + + # initialise current network in the ensemble + model = algo_factory( + algo_name=config.algo_name, + config=config, + obs_key_shapes=shape_meta["all_shapes"], + ac_dim=shape_meta["ac_dim"], + device=device, + ) + print("\n============= Model Summary =============") + print(model) # print model summary + print("") + + for epoch in range(1, config.train.num_epochs + 1): # epoch numbers start at 1 + step_log = TrainUtils.run_epoch(model=model, data_loader=train_loader, epoch=epoch, num_steps=train_num_steps) + model.on_epoch_end(epoch) + + # setup checkpoint path + epoch_ckpt_name = f"model_epoch_{epoch}" + + # check for recurring checkpoint saving conditions + should_save_ckpt = False + if config.experiment.save.enabled: + time_check = (config.experiment.save.every_n_seconds is not None) and ( + time.time() - last_ckpt_time > config.experiment.save.every_n_seconds + ) + epoch_check = ( + (config.experiment.save.every_n_epochs is not None) + and (epoch > 0) + and (epoch % config.experiment.save.every_n_epochs == 0) + ) + epoch_list_check = epoch in config.experiment.save.epochs + should_save_ckpt = time_check or epoch_check or epoch_list_check + ckpt_reason = None + if should_save_ckpt: + last_ckpt_time = time.time() + ckpt_reason = "time" + + print(f"Train Epoch {epoch}") + print(json.dumps(step_log, sort_keys=True, indent=4)) + for k, v in step_log.items(): + if k.startswith("Time_"): + data_logger.record(f"Timing_Stats/Train_{k[5:]}", v, epoch) + else: + data_logger.record(f"Train/{k}", v, epoch) + + # Evaluate the model on validation set + if config.experiment.validate: + with torch.no_grad(): + step_log = TrainUtils.run_epoch( + model=model, data_loader=valid_loader, epoch=epoch, validate=True, num_steps=valid_num_steps + ) + for k, v in step_log.items(): + if k.startswith("Time_"): + data_logger.record(f"Timing_Stats/Valid_{k[5:]}", v, epoch) + else: + data_logger.record(f"Valid/{k}", v, epoch) + + print(f"Validation Epoch {epoch}") + print(json.dumps(step_log, sort_keys=True, indent=4)) + + # save checkpoint if achieve new best validation loss + valid_check = "Loss" in step_log + if valid_check and (best_valid_loss is None or (step_log["Loss"] <= best_valid_loss)): + best_valid_loss = step_log["Loss"] + if config.experiment.save.enabled and config.experiment.save.on_best_validation: + epoch_ckpt_name += f"_best_validation_{best_valid_loss}" + should_save_ckpt = True + ckpt_reason = "valid" if ckpt_reason is None else ckpt_reason + + # Save model checkpoints based on conditions (success rate, validation loss, etc) + + if should_save_ckpt: + TrainUtils.save_model( + model=model, + config=config, + env_meta=env_meta, + shape_meta=shape_meta, + ckpt_path=os.path.join(ckpt_dir, epoch_ckpt_name + ".pth"), + obs_normalization_stats=obs_normalization_stats, + ) + + # Finally, log memory usage in MB + process = psutil.Process(os.getpid()) + mem_usage = int(process.memory_info().rss / 1000000) + data_logger.record("System/RAM Usage (MB)", mem_usage, epoch) + print(f"\nEpoch {epoch} Memory Usage: {mem_usage} MB\n") + + #config.algo.transformer.num_layers = config.algo.transformer.num_layers + 1 + #config.algo.transformer.num_heads = config.algo.transformer.num_heads + 1 + # run.finish() + # terminate logging + data_logger.close() + +def keep_data_percentage(trainset, percentage): + og_len = len(trainset) + # keep only x% of the samples + total_size = len(trainset) + num_samples = int(total_size * (percentage)) + indices = np.random.choice(total_size, num_samples, replace=False) + + trainset = Subset(trainset, indices) + + print(f"size of trainset after reduction is {len(trainset)}") + + new_len = len(trainset) + retained_percent = (new_len / og_len) * 100 + + print(f"Retained: {retained_percent:.2f}% of original dataset") + + return trainset + + + +def main(args: argparse.Namespace): + """Train a model on a task using a specified algorithm. + + Args: + args: Command line arguments. + """ + # load config + if args.task is not None: + # obtain the configuration entry point + cfg_entry_point_key = f"robomimic_{args.algo}_cfg_entry_point" + + print(f"Loading configuration for task: {args.task}") + print(gym.envs.registry.keys()) + print(" ") + cfg_entry_point_file = gym.spec(args.task).kwargs.pop(cfg_entry_point_key) + # check if entry point exists + if cfg_entry_point_file is None: + raise ValueError( + f"Could not find configuration for the environment: '{args.task}'." + f" Please check that the gym registry has the entry point: '{cfg_entry_point_key}'." + ) + + with open(cfg_entry_point_file) as f: + ext_cfg = json.load(f) + config = config_factory(ext_cfg["algo_name"]) + # update config with external json - this will throw errors if + # the external config has keys not present in the base algo config + with config.values_unlocked(): + config.update(ext_cfg) + else: + raise ValueError("Please provide a task name through CLI arguments.") + + if args.dataset is not None: + config.train.data = args.dataset + + if args.name is not None: + config.experiment.name = args.name + # little fix for WANDB + for stream in (sys.stdout, sys.stderr): + if not hasattr(stream, "isatty"): + stream.isatty = lambda: False + os.environ["WANDB_MODE"] = "offline" + run=wandb.init( + project=config.experiment.logging.wandb_proj_name, + name=config.experiment.name + ) + # run.define_metric("train reward", step_metric="episode") + # run.define_metric("episode error", step_metric="episode") + + # change location of experiment directory + config.train.output_dir = os.path.abspath(os.path.join("./logs", args.log_dir, args.task)) + original_output_dir = config.train.output_dir + + log_dirs, ckpt_dirs, video_dirs = [], [], [] + + for i in range(args.ensemble_size): + new_output_dir = f"{original_output_dir}/model{i}/" + config.train.output_dir = new_output_dir + log_dir, ckpt_dir, video_dir = TrainUtils.get_exp_dir(config) + + log_dirs.append(log_dir) + ckpt_dirs.append(ckpt_dir) + video_dirs.append(video_dir) + + print("=============== Ensemble Paths =============== ") + print(f"log_dirs: {log_dirs}") + print(f"ckpt_dirs: {ckpt_dirs}") + print(f"video_dirs: {video_dirs}") + + if args.normalize_training_actions: + config.train.data = normalize_hdf5_actions(config, log_dir) + + # get torch device + device = TorchUtils.get_torch_device(try_to_use_cuda=config.train.cuda) + + config.lock() + + # catch error during training and print it + res_str = "finished run successfully!" + try: + #train(config, device, log_dir, ckpt_dir, video_dir) + train( + config=config, + device=device, + log_dirs=log_dirs, ckpt_dirs=ckpt_dirs, video_dirs=video_dirs, + use_config_seed=True, + ensemble_size=args.ensemble_size + ) + + except Exception as e: + res_str = f"run failed with error:\n{e}\n\n{traceback.format_exc()}" + print(res_str) + run.finish() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + # Experiment Name (for tensorboard, saving models, etc.) + parser.add_argument( + "--name", + type=str, + default=None, + help="(optional) if provided, override the experiment name defined in the config", + ) + + # Dataset path, to override the one in the config + parser.add_argument( + "--dataset", + type=str, + default=None, + help="(optional) if provided, override the dataset path defined in the config", + ) + + parser.add_argument("--task", type=str, default=None, help="Name of the task.") + parser.add_argument("--algo", type=str, default=None, help="Name of the algorithm.") + parser.add_argument("--log_dir", type=str, default="robomimic", help="Path to log directory") + parser.add_argument("--normalize_training_actions", action="store_true", default=False, help="Normalize actions") + + parser.add_argument("--dataset_percentage", type=float, default=1.0, help="Percentage of samples from the dataset to use") + parser.add_argument("--ensemble_size", type=int, default=15, help="Defines the number of networks in the ensemble.") + + args = parser.parse_args() + + # run training + main(args) + # close sim app + simulation_app.close() + + + + # ./isaaclab.sh -p scripts/imitation_learning/robomimic/train.py --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --algo bc --dataset ./docs/training_data/generated_dataset_split.hdf5 --log_dir ./logs/docs/Models/bc/bcc_rnn_gmm_stack_cube_model1/ \ No newline at end of file diff --git a/scripts/tools/convert_mesh.py b/scripts/tools/convert_mesh.py index 50b295397d39..56331c90f7ed 100644 --- a/scripts/tools/convert_mesh.py +++ b/scripts/tools/convert_mesh.py @@ -127,7 +127,7 @@ def main(): usd_dir=os.path.dirname(dest_path), usd_file_name=os.path.basename(dest_path), make_instanceable=args_cli.make_instanceable, - collision_approximation=args_cli.collision_approximation, + # mesh_collision_props=args_cli.collision_approximation, ) # Print info diff --git a/source/isaaclab/isaaclab/envs/mdp/events.py b/source/isaaclab/isaaclab/envs/mdp/events.py index 5c0c967e840c..c0c3afd618bf 100644 --- a/source/isaaclab/isaaclab/envs/mdp/events.py +++ b/source/isaaclab/isaaclab/envs/mdp/events.py @@ -133,7 +133,7 @@ def randomize_rigid_body_scale( # set the new scale scale_spec.default = Gf.Vec3f(*rand_samples[i]) - + print(f"Set object scale : {rand_samples[i]}") # ensure the operation is done in the right ordering if we created the scale attribute. # otherwise, we assume the scale attribute is already in the right order. # note: by default isaac sim follows this ordering for the transform stack so any asset @@ -1300,6 +1300,7 @@ def reset_scene_to_default(env: ManagerBasedEnv, env_ids: torch.Tensor, reset_jo # obtain default and deal with the offset for env origins default_root_state = rigid_object.data.default_root_state[env_ids].clone() default_root_state[:, 0:3] += env.scene.env_origins[env_ids] + #print(f"[RESET] Scene RESET, obj {rigid_object} orientation {default_root_state}") # set into the physics simulation rigid_object.write_root_pose_to_sim(default_root_state[:, :7], env_ids=env_ids) rigid_object.write_root_velocity_to_sim(default_root_state[:, 7:], env_ids=env_ids) diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/__init__.py b/source/isaaclab_mimic/isaaclab_mimic/envs/__init__.py index bc573b58d516..544d80075af4 100644 --- a/source/isaaclab_mimic/isaaclab_mimic/envs/__init__.py +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/__init__.py @@ -16,7 +16,9 @@ from .franka_stack_ik_rel_skillgen_env_cfg import FrankaCubeStackIKRelSkillgenEnvCfg from .franka_stack_ik_rel_visuomotor_cosmos_mimic_env_cfg import FrankaCubeStackIKRelVisuomotorCosmosMimicEnvCfg from .franka_stack_ik_rel_visuomotor_mimic_env_cfg import FrankaCubeStackIKRelVisuomotorMimicEnvCfg - +from .cube_blueprint_mimic_env_cfg import CubeBlueprintMimicEnvCfg +from .cube_mimic_env_cfg import CubeMimicEnvCfg +from .cube_mimic_env import CubeMimicEnv ## # Inverse Kinematics - Relative Pose Control ## @@ -167,3 +169,23 @@ }, disable_env_checker=True, ) + +###### cube stuff + +gym.register( + id="Cube-Mimic-v0", + entry_point="isaaclab_mimic.envs:CubeMimicEnv", + kwargs={ + "env_cfg_entry_point": cube_mimic_env_cfg.CubeMimicEnvCfg, + }, + disable_env_checker=True, +) + +gym.register( + id="Cube-Blueprint-Mimic-v0", + entry_point="isaaclab_mimic.envs:CubeMimicEnv", + kwargs={ + "env_cfg_entry_point": cube_blueprint_mimic_env_cfg.CubeBlueprintMimicEnvCfg, + }, + disable_env_checker=True, +) \ No newline at end of file diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_blueprint_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_blueprint_mimic_env_cfg.py new file mode 100755 index 000000000000..698abcab7c7a --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_blueprint_mimic_env_cfg.py @@ -0,0 +1,150 @@ +# Copyright (c) 2024-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.cube_lift.config.franka.dev_ik_rel_env_cfg import FrankaDevEnvCfg + + +@configclass +class CubeBlueprintMimicEnvCfg(FrankaDevEnvCfg, MimicEnvCfg): + """ + Isaac Lab Mimic environment config class for Franka Cube Stack IK Rel env. + """ + + def __post_init__(self): + # post init of parents + super().__post_init__() + + # Override the existing values + self.datagen_config.name = "beaker_rand_scale_blueprint_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = False + self.datagen_config.generation_num_trials = 10 + self.datagen_config.generation_select_src_per_subtask = True + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.max_num_failures = 25 + self.datagen_config.seed = 1 + + # The following are the subtask configurations for the stack task. + subtask_configs = [] + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # This key corresponds to the binary indicator in "datagen_info" that signals + # when this subtask is finished (e.g., on a 0 to 1 edge). + subtask_term_signal="appr", + # Specifies time offsets for data generation when splitting a trajectory into + # subtask segments. Random offsets are added to the termination boundary. + subtask_term_offset_range=(0, 0), + # Selection strategy for the source subtask segment during data generation + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="grasp", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 10), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + #lift ? + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # End of final subtask does not need to be detected + subtask_term_signal="lift", + # No time offsets for the final subtask + subtask_term_offset_range=(5, 10), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_robot_distance", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # End of final subtask does not need to be detected + # LOL YOU DO + subtask_term_signal="appr_goal", + # No time offsets for the final subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_robot_distance", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + # subtask_configs.append( + # SubTaskConfig( + # # Each subtask involves manipulation with respect to a single object frame. + # object_ref="object", + # # End of final subtask does not need to be detected + # # LOL YOU DO + # subtask_term_signal="release_object", + # # No time offsets for the final subtask + # subtask_term_offset_range=(0, 0), + # # Selection strategy for source subtask segment + # selection_strategy="nearest_neighbor_object", + # # Optional parameters for the selection strategy function + # selection_strategy_kwargs={"nn_k": 3}, + # # Amount of action noise to apply during this subtask + # action_noise=0.03, + # # Number of interpolation steps to bridge to this subtask segment + # num_interpolation_steps=5, + # # Additional fixed steps for the robot to reach the necessary pose + # num_fixed_steps=0, + # # If True, apply action noise during the interpolation phase and execution + # apply_noise_during_interpolation=False, + # ) + # ) + self.subtask_configs["franka"] = subtask_configs \ No newline at end of file diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env.py b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env.py new file mode 100755 index 000000000000..a295df1c7fce --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env.py @@ -0,0 +1,165 @@ + +# Copyright (c) 2024-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +import torch +from collections.abc import Sequence + +import isaaclab.utils.math as PoseUtils +from isaaclab.envs import ManagerBasedRLMimicEnv + + +class CubeMimicEnv(ManagerBasedRLMimicEnv): + """ + Isaac Lab Mimic environment wrapper class for Franka Cube Stack IK Rel env. + """ + + def get_robot_eef_pose(self, eef_name: str, env_ids: Sequence[int] | None = None) -> torch.Tensor: + """ + Get current robot end effector pose. Should be the same frame as used by the robot end-effector controller. + + Args: + eef_name: Name of the end effector. + env_ids: Environment indices to get the pose for. If None, all envs are considered. + + Returns: + A torch.Tensor eef pose matrix. Shape is (len(env_ids), 4, 4) + """ + if env_ids is None: + env_ids = slice(None) + + # Retrieve end effector pose from the observation buffer + eef_pos = self.obs_buf["policy"]["eef_pos"][env_ids] + eef_quat = self.obs_buf["policy"]["eef_quat"][env_ids] + + # Quaternion format is w,x,y,z + return PoseUtils.make_pose(eef_pos, PoseUtils.matrix_from_quat(eef_quat)) + + def target_eef_pose_to_action( + self, target_eef_pose_dict: dict, gripper_action_dict: dict, noise: float | None = None, env_id: int = 0 + ) -> torch.Tensor: + """ + Takes a target pose and gripper action for the end effector controller and returns an action + (usually a normalized delta pose action) to try and achieve that target pose. + Noise is added to the target pose action if specified. + + Args: + target_eef_pose_dict: Dictionary of 4x4 target eef pose for each end-effector. + gripper_action_dict: Dictionary of gripper actions for each end-effector. + noise: Noise to add to the action. If None, no noise is added. + env_id: Environment index to get the action for. + + Returns: + An action torch.Tensor that's compatible with env.step(). + """ + eef_name = list(self.cfg.subtask_configs.keys())[0] + + # target position and rotation + (target_eef_pose,) = target_eef_pose_dict.values() + target_pos, target_rot = PoseUtils.unmake_pose(target_eef_pose) + + # current position and rotation + curr_pose = self.get_robot_eef_pose(eef_name, env_ids=[env_id])[0] + curr_pos, curr_rot = PoseUtils.unmake_pose(curr_pose) + + # normalized delta position action + delta_position = target_pos - curr_pos + + # normalized delta rotation action + delta_rot_mat = target_rot.matmul(curr_rot.transpose(-1, -2)) + delta_quat = PoseUtils.quat_from_matrix(delta_rot_mat) + delta_rotation = PoseUtils.axis_angle_from_quat(delta_quat) + + # get gripper action for single eef + (gripper_action,) = gripper_action_dict.values() + + # add noise to action + pose_action = torch.cat([delta_position, delta_rotation], dim=0) + if noise is not None: + noise = noise * torch.randn_like(pose_action) + pose_action += noise + pose_action = torch.clamp(pose_action, -1.0, 1.0) + + return torch.cat([pose_action, gripper_action], dim=0) + + def action_to_target_eef_pose(self, action: torch.Tensor) -> dict[str, torch.Tensor]: + """ + Converts action (compatible with env.step) to a target pose for the end effector controller. + Inverse of @target_eef_pose_to_action. Usually used to infer a sequence of target controller poses + from a demonstration trajectory using the recorded actions. + + Args: + action: Environment action. Shape is (num_envs, action_dim) + + Returns: + A dictionary of eef pose torch.Tensor that @action corresponds to + """ + eef_name = list(self.cfg.subtask_configs.keys())[0] + + delta_position = action[:, :3] + delta_rotation = action[:, 3:6] + + # current position and rotation + curr_pose = self.get_robot_eef_pose(eef_name, env_ids=None) + curr_pos, curr_rot = PoseUtils.unmake_pose(curr_pose) + + # get pose target + target_pos = curr_pos + delta_position + + # Convert delta_rotation to axis angle form + delta_rotation_angle = torch.linalg.norm(delta_rotation, dim=-1, keepdim=True) + delta_rotation_axis = delta_rotation / delta_rotation_angle + + # Handle invalid division for the case when delta_rotation_angle is close to zero + is_close_to_zero_angle = torch.isclose(delta_rotation_angle, torch.zeros_like(delta_rotation_angle)).squeeze(1) + delta_rotation_axis[is_close_to_zero_angle] = torch.zeros_like(delta_rotation_axis)[is_close_to_zero_angle] + + delta_quat = PoseUtils.quat_from_angle_axis(delta_rotation_angle.squeeze(1), delta_rotation_axis).squeeze(0) + delta_rot_mat = PoseUtils.matrix_from_quat(delta_quat) + target_rot = torch.matmul(delta_rot_mat, curr_rot) + + target_poses = PoseUtils.make_pose(target_pos, target_rot).clone() + + return {eef_name: target_poses} + + def actions_to_gripper_actions(self, actions: torch.Tensor) -> dict[str, torch.Tensor]: + """ + Extracts the gripper actuation part from a sequence of env actions (compatible with env.step). + + Args: + actions: environment actions. The shape is (num_envs, num steps in a demo, action_dim). + + Returns: + A dictionary of torch.Tensor gripper actions. Key to each dict is an eef_name. + """ + # last dimension is gripper action + return {list(self.cfg.subtask_configs.keys())[0]: actions[:, -1:]} + + def get_subtask_term_signals(self, env_ids: Sequence[int] | None = None) -> dict[str, torch.Tensor]: + """ + Gets a dictionary of termination signal flags for each subtask in a task. The flag is 1 + when the subtask has been completed and 0 otherwise. The implementation of this method is + required if intending to enable automatic subtask term signal annotation when running the + dataset annotation tool. This method can be kept unimplemented if intending to use manual + subtask term signal annotation. + + Args: + env_ids: Environment indices to get the termination signals for. If None, all envs are considered. + + Returns: + A dictionary termination signal flags (False or True) for each subtask. + """ + if env_ids is None: + env_ids = slice(None) + + signals = dict() + subtask_terms = self.obs_buf["subtask_terms"] + #signals["appr"] = subtask_terms["appr"][env_ids] + signals["grasp"] = subtask_terms["grasp"][env_ids] + #signals["lift"] = subtask_terms["lift"][env_ids] + signals["appr_goal"] = subtask_terms["appr_goal"][env_ids] + # signals["release_object"] = subtask_terms["release_object"][env_ids] + # final subtask is placing cubeC on cubeA (motion relative to cubeA) - but final subtask signal is not needed + return signals diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py new file mode 100755 index 000000000000..982b1320bac6 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py @@ -0,0 +1,132 @@ +# Copyright (c) 2024-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.cube_lift.config.franka.dev_ik_rel_env_cfg import FrankaDevEnvCfg + + +@configclass +class CubeMimicEnvCfg(FrankaDevEnvCfg, MimicEnvCfg): + """ + Isaac Lab Mimic environment config class for Franka Cube Stack IK Rel env. + """ + + def __post_init__(self): + # post init of parents + super().__post_init__() + # # TODO: Figure out how we can move this to the MimicEnvCfg class + # # The __post_init__() above only calls the init for FrankaCubeStackEnvCfg and not MimicEnvCfg + # # https://stackoverflow.com/questions/59986413/achieving-multiple-inheritance-using-python-dataclasses + + # Override the existing values + self.datagen_config.name = "beaker_rand_scale_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = False + self.datagen_config.generation_num_trials = 100 + self.datagen_config.generation_select_src_per_subtask = True + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.max_num_failures = 100 + self.datagen_config.seed = 101 + + # The following are the subtask configurations for the stack task. + # The following are the subtask configurations for the stack task. + subtask_configs = [] + # subtask_configs.append( + # SubTaskConfig( + # # Each subtask involves manipulation with respect to a single object frame. + # object_ref="object", + # # This key corresponds to the binary indicator in "datagen_info" that signals + # # when this subtask is finished (e.g., on a 0 to 1 edge). + # subtask_term_signal="appr", + # # Specifies time offsets for data generation when splitting a trajectory into + # # subtask segments. Random offsets are added to the termination boundary. + # subtask_term_offset_range=(0, 0), + # # Selection strategy for the source subtask segment during data generation + # selection_strategy="nearest_neighbor_object", + # # Optional parameters for the selection strategy function + # selection_strategy_kwargs={"nn_k": 3}, + # # Amount of action noise to apply during this subtask + # action_noise=0.03, + # # Number of interpolation steps to bridge to this subtask segment + # num_interpolation_steps=0, + # # Additional fixed steps for the robot to reach the necessary pose + # num_fixed_steps=0, + # # If True, apply action noise during the interpolation phase and execution + # apply_noise_during_interpolation=False, + # ) + # ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="grasp", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + #lift ? + # subtask_configs.append( + # SubTaskConfig( + # # Each subtask involves manipulation with respect to a single object frame. + # object_ref="object", + # # End of final subtask does not need to be detected + # subtask_term_signal="lift", + # # No time offsets for the final subtask + # subtask_term_offset_range=(0, 0), + # # Selection strategy for source subtask segment + # selection_strategy="nearest_neighbor_robot_distance", + # # Optional parameters for the selection strategy function + # selection_strategy_kwargs={"nn_k": 3}, + # # Amount of action noise to apply during this subtask + # action_noise=0.03, + # # Number of interpolation steps to bridge to this subtask segment + # num_interpolation_steps=0, + # # Additional fixed steps for the robot to reach the necessary pose + # num_fixed_steps=0, + # # If True, apply action noise during the interpolation phase and execution + # apply_noise_during_interpolation=False, + # ) + # ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # End of final subtask does not need to be detected + # LOL YOU DO + subtask_term_signal="appr_goal", + # No time offsets for the final subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_robot_distance", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + + self.subtask_configs["franka"] = subtask_configs \ No newline at end of file diff --git a/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/curobo_planner_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/curobo_planner_cfg.py index 6755093f7046..eaf28e0ed153 100644 --- a/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/curobo_planner_cfg.py +++ b/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/curobo_planner_cfg.py @@ -117,7 +117,7 @@ class CuroboPlannerCfg: enable_graph_attempt: int = 5 """Number of graph planning attempts.""" - max_planning_attempts: int = 15 + max_planning_attempts: int = 100 """Maximum number of planning attempts.""" enable_finetune_trajopt: bool = True @@ -391,14 +391,14 @@ def franka_config(cls) -> "CuroboPlannerCfg": grasp_gripper_open_val=0.04, approach_distance=0.0, retreat_distance=0.0, - max_planning_attempts=1, + max_planning_attempts=100, time_dilation_factor=0.6, enable_finetune_trajopt=True, n_repeat=None, motion_step_size=None, visualize_spheres=False, visualize_plan=False, - debug_planner=False, + debug_planner=True, sphere_update_freq=5, motion_noise_scale=0.02, # World extraction tuning for Franka envs @@ -436,6 +436,22 @@ def franka_stack_cube_config(cls) -> "CuroboPlannerCfg": config.surface_sphere_radius = 0.01 config.get_world_config = lambda: config._get_world_config_with_table_adjustment() return config + + ### Add in Chem pickup style + @classmethod + def franka_beaker_config(cls) -> "CuroboPlannerCfg": + """Create configuration for Franka stacking a normal cube.""" + config = cls.franka_config() + config.static_objects = ["table"] + config.visualize_plan = True + config.debug_planner = True + config.motion_noise_scale = 0.02 + config.collision_activation_distance = 0.0 + config.approach_distance = 0.05 + config.retreat_distance = 0.05 + config.surface_sphere_radius = 0.005 + config.get_world_config = lambda: config._get_world_config_with_table_adjustment() + return config @classmethod def from_task_name(cls, task_name: str) -> "CuroboPlannerCfg": @@ -453,6 +469,8 @@ def from_task_name(cls, task_name: str) -> "CuroboPlannerCfg": return cls.franka_stack_cube_bin_config() elif "stack-cube" in task_lower: return cls.franka_stack_cube_config() + elif "Cube-Mimic" in task_lower: + return cls.franka_beaker_config() else: # Default to Franka configuration print(f"Warning: Unknown robot in task '{task_name}', using Franka configuration") diff --git a/source/isaaclab_mimic/isaaclab_mimic/motion_planners/motion_planner_base.py b/source/isaaclab_mimic/isaaclab_mimic/motion_planners/motion_planner_base.py index 783363b73300..31cae876ab32 100644 --- a/source/isaaclab_mimic/isaaclab_mimic/motion_planners/motion_planner_base.py +++ b/source/isaaclab_mimic/isaaclab_mimic/motion_planners/motion_planner_base.py @@ -36,7 +36,7 @@ class MotionPlannerBase(ABC): """ def __init__( - self, env: ManagerBasedEnv, robot: Articulation, env_id: int = 0, debug: bool = False, **kwargs + self, env: ManagerBasedEnv, robot: Articulation, env_id: int = 0, debug: bool = True, **kwargs ) -> None: """Initialize the motion planner. diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/__init__.py new file mode 100755 index 000000000000..fd33aacd3a5b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configurations for the object lift environments.""" + +# We leave this file empty since we don't want to expose any configs in this package directly. +# We still need this file to import the "config" module in the parent package. diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/__init__.py new file mode 100755 index 000000000000..fd33aacd3a5b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configurations for the object lift environments.""" + +# We leave this file empty since we don't want to expose any configs in this package directly. +# We still need this file to import the "config" module in the parent package. diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/__init__.py new file mode 100755 index 000000000000..14d95703522d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/__init__.py @@ -0,0 +1,74 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +import gymnasium as gym +import os + +from . import agents + +## +# Register Gym environments. +## + +## +# Joint Position Control +## + + + +## +# Inverse Kinematics - Absolute Pose Control +## + +# gym.register( +# id="Isaac-Lift-Teddy-Bear-Franka-IK-Abs-v0", +# entry_point="isaaclab.envs:ManagerBasedRLEnv", +# kwargs={ +# "env_cfg_entry_point": f"{__name__}.ik_abs_env_cfg:FrankaTeddyBearLiftEnvCfg", +# }, +# disable_env_checker=True, +# ) + +## +# Inverse Kinematics - Relative Pose Control +## + + +# gym.register( +# id="Dev-IK-Abs-v0", +# entry_point="isaaclab.envs:ManagerBasedRLEnv", +# kwargs={ +# "env_cfg_entry_point": f"{__name__}.dev_env_cfg:FrankaDevEnvCfg", +# "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), +# }, +# disable_env_checker=True, +# ) + +gym.register( + id="Pour-IK-Rel-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.pour_ik_rel_env_cfg:FrankaPourEnvCfg", + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), + "robomimic_bc_trans_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_trans.json"), + "robomimic_hbc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/hbc.json"), + "robomimic_bcq_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bcq.json"), + #"robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc.json"), + }, + disable_env_checker=True, +) +# gym.register( +# id="Dev-IK-Rel-v1", +# entry_point="isaaclab.envs:ManagerBasedRLEnv", +# kwargs={ +# "env_cfg_entry_point": f"{__name__}.dev_ik_rel_env_obs:FrankaDevEnvCfg", +# "robomimic_bc_rnn_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), +# "robomimic_bc_trans_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_trans.json"), +# "robomimic_hbc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/hbc.json"), +# "robomimic_bcq_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bcq.json"), +# "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc.json"), +# }, +# disable_env_checker=True, +# ) + diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/__init__.py new file mode 100755 index 000000000000..e75ca2bc3f90 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/rl_games_ppo_cfg.yaml new file mode 100755 index 000000000000..6ed68d88912c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,79 @@ +params: + seed: 42 + + # environment wrapper clipping + env: + clip_observations: 100.0 + clip_actions: 100.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [256, 128, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: franka_lift + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + value_bootstrap: False + num_actors: -1 + reward_shaper: + scale_value: 0.01 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 1e-4 + lr_schedule: adaptive + schedule_type: legacy + kl_threshold: 0.01 + score_to_win: 100000000 + max_epochs: 1500 + save_best_after: 100 + save_frequency: 50 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.001 + truncate_grads: True + e_clip: 0.2 + horizon_length: 24 + minibatch_size: 24576 + mini_epochs: 8 + critic_coef: 4 + clip_value: True + clip_actions: False + seq_len: 4 + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/bc.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/bc.json new file mode 100755 index 000000000000..3108d71b52d3 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/bc.json @@ -0,0 +1,135 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_202507210930", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "pick-place-tabletop" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 100, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": true, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": null, + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "terminate_on_success" : true, + "n": 50, + "horizon": 400, + "rate": 50 + } + }, + "train": { + "data": null, + "output_dir": "/training_runs/bc", + "num_data_workers": 1, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_normalize_obs": false, + "hdf5_filter_key": "train", + "hdf5_validation_filter_key": "valid", + "seq_length": 10, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 3000, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.01, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gmm": { + "enabled": true, + "num_modes": 10, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false + }, + "rnn": { + "enabled": false, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object_position", + "target_object_position", + "joint_pos", + "joint_vel", + "actions" + ], + "rgb": [], + "depth": [], + "scan": [] + } + } + } +} \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/bc_rnn_low_dim.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/bc_rnn_low_dim.json new file mode 100755 index 000000000000..6b09227a280f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/bc_rnn_low_dim.json @@ -0,0 +1,135 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_new_demos", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "Lift_beaker_new_demos" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": null, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": true, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": "Dev-IK-Rel-v0", + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "terminate_on_success" : true, + "n": 50, + "horizon": 400, + "rate": 50 + } + }, + "train": { + "data": null, + "output_dir": "/training_runs/bc_rnn", + 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b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/bc_trans.json @@ -0,0 +1,133 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_trans_20250722_frame_20", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "pick-place-tabletop" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 100, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": true, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": "Dev-IK-Rel-v0", + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "terminate_on_success" : true, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0 + } + }, + "train": { + "data": null, + "output_dir": 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b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/bcq.json @@ -0,0 +1,308 @@ +{ + "algo_name": "bcq", + "experiment": { + "name": "bcq", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "pick-place-tabletop" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 100, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": false + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": null, + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": null, + "output_dir": "/training_runs/bcq", + "num_data_workers": 1, + 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@@ +{ + "algo_name": "hbc", + "experiment": { + "name": "hbc_202507211400", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "pick-place-tabletop" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 50, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": false, + "on_best_rollout_success_rate"` : false + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": "Dev-IK-Rel-v0", + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": null, + "output_dir": "../hbc_trained_models", + "num_data_workers": 0, + "hdf5_cache_mode": "all", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": true, + "hdf5_normalize_obs": false, + "hdf5_filter_key": "train", + "hdf5_validation_filter_key": "valid", + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 1 + }, + "algo": { + "mode": "separate", + "actor_use_random_subgoals": false, + "subgoal_update_interval": 10, + "latent_subgoal": { + "enabled": false, + "prior_correction": { + "enabled": false, + "num_samples": 100 + } + }, + "planner": { + "optim_params": { + "goal_network": { + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.1, + "epoch_schedule": [] + }, + "regularization": { + "L2": 0.0 + } + } + }, + "subgoal_horizon": 10, + "ae": { + "planner_layer_dims": [ + 300, + 400 + ] + }, + "vae": { + "enabled": true, + "latent_dim": 16, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + } + }, + "actor": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.1, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [ + 1024, + 1024 + ], + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + } + }, + "observation": { + "planner": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object_position", + "target_object_position", + + "joint_pos", + "joint_vel", + "actions" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + }, + "subgoal": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object_position", + "target_object_position", + "joint_pos", + "joint_vel", + "actions" + ], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + }, + "actor": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object_position", + "target_object_position", + "joint_pos", + "joint_vel", + "actions" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + } + }, + "meta": { + "hp_base_config_file": null, + "hp_keys": [], + "hp_values": [] + } +} \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/rsl_rl_ppo_cfg.py new file mode 100755 index 000000000000..e3fb13e1d1f7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,37 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class LiftCubePPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 1500 + save_interval = 50 + experiment_name = "franka_lift" + empirical_normalization = False + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_hidden_dims=[256, 128, 64], + critic_hidden_dims=[256, 128, 64], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.006, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-4, + schedule="adaptive", + gamma=0.98, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/sb3_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/sb3_ppo_cfg.yaml new file mode 100755 index 000000000000..6d6f15781acc --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/sb3_ppo_cfg.yaml @@ -0,0 +1,28 @@ +# Reference: https://github.com/DLR-RM/rl-baselines3-zoo/blob/master/hyperparams/ppo.yml#L32 +seed: 42 + +# epoch * n_steps * nenvs: 500×512*8*8 +n_timesteps: 16384000 +policy: 'MlpPolicy' +n_steps: 64 +# mini batch size: num_envs * nsteps / nminibatches 2048×512÷2048 +batch_size: 192 +gae_lambda: 0.95 +gamma: 0.99 +n_epochs: 8 +ent_coef: 0.00 +vf_coef: 0.0001 +learning_rate: !!float 3e-4 +clip_range: 0.2 +policy_kwargs: "dict( + activation_fn=nn.ELU, + net_arch=dict(pi=[256, 128, 64], vf=[256, 128, 64]) + )" +target_kl: 0.01 +max_grad_norm: 1.0 + +# # Uses VecNormalize class to normalize obs +# normalize_input: True +# # Uses VecNormalize class to normalize rew +# normalize_value: True +# clip_obs: 5 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/skrl_ppo_cfg.yaml new file mode 100755 index 000000000000..94337d46bac6 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,80 @@ +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: STATES + layers: [256, 128, 64] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: STATES + layers: [256, 128, 64] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 8 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.001 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.01 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "franka_lift" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 36000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/dev_osc_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/dev_osc_cfg.py new file mode 100755 index 000000000000..bb1d4bd550e2 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/dev_osc_cfg.py @@ -0,0 +1,114 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.assets import RigidObjectCfg, ArticulationCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab_tasks.manager_based.manipulation.cube_lift import mdp +from isaaclab_tasks.manager_based.manipulation.cube_lift.lift_env_cfg import CubeEnvCfg +from isaaclab_assets.glassware.glassware_objects import Chem_Assets + + +import math +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + +@configclass +class FrankaDevEnvCfg(CubeEnvCfg): + def __post_init__(self): + # post init of parent + + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace( + prim_path="{ENV_REGEX_NS}/Robot", + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + + "panda_joint1": 0.405, + "panda_joint2": 0.35, + "panda_joint3": -0.22, + "panda_joint4": -3.0, + "panda_joint5": -2.85, + "panda_joint6": math.pi / 2, # +90° → keeps hand level + "panda_joint7": 0.9, + "panda_finger_joint1": 0.04, # open gripper + "panda_finger_joint2": 0.04, + } + ), + ) + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls"), + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + + self.actions.gripper_action = mdp.BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["panda_finger.*"], + open_command_expr={"panda_finger_.*": 0.04}, + close_command_expr={"panda_finger_.*": 0.0}, + ) + # Set the body name for the end effector + self.commands.object_pose.body_name = "panda_hand" + self.commands.object_pose.body_name = "panda_hand" + + glassware = Chem_Assets() + # Set each stacking cube deterministically + self.scene.object = glassware.beaker(pos=[0.4, 0.0, 0.0203],name="Object") + #### everything else leave default + # self.scene.flask = glassware.flask() + # self.scene.vial = glassware.vial() + # self.scene.beaker = glassware.beaker() + self.scene.stirplate = glassware.stirplate() + #self.scene.random = glassware.random_object() + + # Listens to the required transforms + marker_cfg = FRAME_MARKER_CFG.copy() + marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + marker_cfg.prim_path = "/Visuals/FrameTransformer" + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_link0", + debug_vis=False, + visualizer_cfg=marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_hand", + name="end_effector", + offset=OffsetCfg( + pos=[0.0, 0.0, 0.1034], + + ), + ), + ], + ) + + +@configclass +class FrankaDevEnvCfg_PLAY(FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/ik_abs_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/ik_abs_env_cfg.py new file mode 100755 index 000000000000..255b5b55eb86 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/ik_abs_env_cfg.py @@ -0,0 +1,128 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.assets import DeformableObjectCfg +from isaaclab.assets import RigidObjectCfg, ArticulationCfg +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sim.spawners import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +import isaaclab_tasks.manager_based.manipulation.cube_lift.mdp as mdp + +from . import joint_pos_env_cfg +import math +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +## +# Rigid object lift environment. +## + + +@configclass +class FrankaCubeEnvCfg(joint_pos_env_cfg.FrankaCubeEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace( + prim_path="{ENV_REGEX_NS}/Robot", + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + + "panda_joint1": 0.405, + "panda_joint2": 0.35, + "panda_joint3": -0.22, + "panda_joint4": -3.0, + "panda_joint5": -2.85, + "panda_joint6": math.pi / 2, # +90° → keeps hand level + "panda_joint7": 0.9, + "panda_finger_joint1": 0.04, # open gripper + "panda_finger_joint2": 0.04, + } + ), + ) + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls"), + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + print("action shape: ", self.actions.arm_action.shape) + + +@configclass +class FrankaCubeEnvCfg_PLAY(FrankaCubeEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False + + +## +# Deformable object lift environment. +## + + +@configclass +class FrankaTeddyBearLiftEnvCfg(FrankaCubeLiftEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + self.scene.object = DeformableObjectCfg( + prim_path="{ENV_REGEX_NS}/Object", + init_state=DeformableObjectCfg.InitialStateCfg(pos=(0.5, 0, 0.05), rot=(0.707, 0, 0, 0.707)), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Objects/Teddy_Bear/teddy_bear.usd", + scale=(0.01, 0.01, 0.01), + ), + ) + + # Make the end effector less stiff to not hurt the poor teddy bear + self.scene.robot.actuators["panda_hand"].effort_limit = 50.0 + self.scene.robot.actuators["panda_hand"].stiffness = 40.0 + self.scene.robot.actuators["panda_hand"].damping = 10.0 + + # Disable replicate physics as it doesn't work for deformable objects + # FIXME: This should be fixed by the PhysX replication system. + self.scene.replicate_physics = False + + # Set events for the specific object type (deformable cube) + self.events.reset_object_position = EventTerm( + func=mdp.reset_nodal_state_uniform, + mode="reset", + params={ + "position_range": {"x": (-0.1, 0.1), "y": (-0.25, 0.25), "z": (0.0, 0.0)}, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("object"), + }, + ) + + # Remove all the terms for the state machine demo + # TODO: Computing the root pose of deformable object from nodal positions is expensive. + # We need to fix that part before enabling these terms for the training. + self.terminations.object_dropping = None + self.rewards.reaching_object = None + self.rewards.lifting_object = None + self.rewards.object_goal_tracking = None + self.rewards.object_goal_tracking_fine_grained = None + self.observations.policy.object_position = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/ik_rel_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/ik_rel_env_cfg.py new file mode 100755 index 000000000000..1335fa8cf984 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/ik_rel_env_cfg.py @@ -0,0 +1,65 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass +from isaaclab.assets import RigidObjectCfg, ArticulationCfg +from . import joint_pos_env_cfg +import math +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +@configclass +class FrankaCubeEnvCfg(joint_pos_env_cfg.FrankaCubeEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + print("This is the base environment") + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace( + prim_path="{ENV_REGEX_NS}/Robot", + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + + "panda_joint1": 0.405, + "panda_joint2": 0.35, + "panda_joint3": -0.22, + "panda_joint4": -3.0, + "panda_joint5": -2.85, + "panda_joint6": math.pi / 2, # +90° → keeps hand level + "panda_joint7": 0.9, + "panda_finger_joint1": 0.04, # open gripper + "panda_finger_joint2": 0.04, + } + ), + ) + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + + +@configclass +class FrankaCubeEnvCfg_PLAY(FrankaCubeEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + print("This is the play environment") + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/joint_pos_env_cfg.py new file mode 100755 index 000000000000..e0ee886bdda2 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/joint_pos_env_cfg.py @@ -0,0 +1,102 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.assets import RigidObjectCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from isaaclab_tasks.manager_based.manipulation.beaker_pour import mdp +from isaaclab_tasks.manager_based.manipulation.beaker_pour.pour_env_cfg import PourEnvCfg +from isaaclab_assets.glassware.glassware_objects import Chem_Assets +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip +from isaaclab_assets.robots.franka import FRANKA_PANDA_CFG # isort: skip + + +@configclass +class FrankaPourEnvCfg(PourEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + self.scene.robot = FRANKA_PANDA_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set actions for the specific robot type (franka) + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + scale=0.5, + use_default_offset=True + ) + self.actions.gripper_action = mdp.BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["panda_finger.*"], + open_command_expr={"panda_finger_.*": 0.04}, + close_command_expr={"panda_finger_.*": 0.0}, + ) + print("Action shape : ", self.actions.__dict__) + # Set the body name for the end effector + self.commands.object_pose.body_name = "panda_hand" + + # Set Cube as object + + #what if its now a vial rack ? + glassware = Chem_Assets() + # Set each stacking cube deterministically + self.scene.object = glassware.beaker(pos=[0.4, 0.0, 0.0203],name="Object") + + + + # Listens to the required transforms + marker_cfg = FRAME_MARKER_CFG.copy() + marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + marker_cfg.prim_path = "/Visuals/FrameTransformer" + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_link0", + debug_vis=False, + visualizer_cfg=marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_hand", + name="end_effector", + offset=OffsetCfg( + pos=[0.0, 0.0, 0.1034], + ), + ), + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_rightfinger", + name="tool_rightfinger", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.046), + ), + ), + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_leftfinger", + name="tool_leftfinger", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.046), + ), + ), + ], + ) + + +@configclass +class FrankaCubeEnvCfg_PLAY(FrankaCubeEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/pour_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/pour_env_cfg.py new file mode 100755 index 000000000000..4001114fe828 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/pour_env_cfg.py @@ -0,0 +1,132 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.assets import RigidObjectCfg, ArticulationCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab_tasks.manager_based.manipulation.beaker_pour import mdp +from isaaclab_tasks.manager_based.manipulation.beaker_pour import PourEnvCfg +#from isaaclab_assets.glassware.glassware_objects import Chem_Assets + + +import math +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + +@configclass +class FrankaDevEnvCfg(PourEnvCfg): + def __post_init__(self): + # post init of parent + + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace( + prim_path="{ENV_REGEX_NS}/Robot", + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + + "panda_joint1": 0.3281, + "panda_joint2": 0.3684, + "panda_joint3": -0.2787, + "panda_joint4": -2.6138, + "panda_joint5": -2.7, + "panda_joint6": 2.4991, # +90° → keeps hand level + "panda_joint7": 0.3331, + "panda_finger_joint1": 0.04, # open gripper + "panda_finger_joint2": 0.04, + } + ), + ) + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls"), + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + + self.actions.gripper_action = mdp.BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["panda_finger.*"], + open_command_expr={"panda_finger_.*": 0.04}, + close_command_expr={"panda_finger_.*": 0.0}, + ) + # Set the body name for the end effector + self.commands.object_pose.body_name = "panda_hand" + self.commands.object_pose.body_name = "panda_hand" + + # glassware = Chem_Assets() + # Set each stacking cube deterministically + #self.scene.object = glassware.beaker(pos=[0.5, 0.0, 0.01],name="Object") + #### everything else leave default + # self.scene.flask = glassware.flask() + # self.scene.vial = glassware.vial() + # self.scene.beaker = glassware.beaker() + #self.scene.stirplate = glassware.stirplate() + #self.scene.random = glassware.random_object() + # self.scene.obstacle = glassware.cube_obs(pos= [0.8, -0.8, 0.0],rot=[0, 0, 1, 0], name="cube_obs") + #self.scene.obstacle = glassware.stirplate(pos= [0.5, -0.32, 0.0],rot=[0.707, 0, 0, -0.707], name="cube_obs") + # self.scene.obstacle = glassware.stirplate(pos= [0.8, -0.8, 0.0], rot=[0.707, 0, 0, -0.707], name="cube_obs") + self.scene.object = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Object", + init_state=RigidObjectCfg.InitialStateCfg( pos=[0.5, 0.0, 0.02],rot=[0, 0, 1, 0]), + spawn=UsdFileCfg( + usd_path=f"source/isaaclab_assets/isaaclab_assets/glassware/beaker/beaker.usd", + scale=(0.5, 0.5, 0.5), + rigid_props=RigidBodyPropertiesCfg( + solver_position_iteration_count=16, + solver_velocity_iteration_count=1, + max_angular_velocity=1000.0, + max_linear_velocity=1000.0, + max_depenetration_velocity=5.0, + disable_gravity=False, + ), + ), + ) + # Listens to the required transforms + marker_cfg = FRAME_MARKER_CFG.copy() + marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + marker_cfg.prim_path = "/Visuals/FrameTransformer" + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_link0", + debug_vis=False, + visualizer_cfg=marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_hand", + name="end_effector", + offset=OffsetCfg( + pos=[0.0, 0.0, 0.1034], + + ), + ), + ], + ) + + +@configclass +class FrankaDevEnvCfg_PLAY(FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/pour_ik_rel_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/pour_ik_rel_env_cfg.py new file mode 100755 index 000000000000..d5a475362fd7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/pour_ik_rel_env_cfg.py @@ -0,0 +1,63 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass +from isaaclab.assets import RigidObjectCfg, ArticulationCfg + +from . import pour_env_cfg +import math +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + + +@configclass +class FrankaDevEnvCfg(pour_env_cfg.FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace( + prim_path="{ENV_REGEX_NS}/Robot", + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + + "panda_joint1": 0.3281, + "panda_joint2": -0.3684, + "panda_joint3": -0.2787, + "panda_joint4": -2.6138, + "panda_joint5": -2.7527, + "panda_joint6": 2.4991, # +90° → keeps hand level + "panda_joint7": 0.3331, + "panda_finger_joint1": 0.04, # open gripper + "panda_finger_joint2": 0.04, + } + ), + ) + # replace with relative position controller + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + ) + + + +@configclass +class FrankaCubeEnvCfg_PLAY(FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/pour_ik_rel_env_obs.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/pour_ik_rel_env_obs.py new file mode 100755 index 000000000000..5507c7fe2447 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/pour_ik_rel_env_obs.py @@ -0,0 +1,49 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass +from isaaclab.assets import RigidObjectCfg + +from . import pour_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip +#from isaaclab_assets.glassware.glassware_objects import Chem_Assets + +@configclass +class FrankaDevEnvCfg(pour_env_cfg.FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # replace with relative position controller + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + # glassware = Chem_Assets() + #self.scene.obstacle = glassware.cube_obs(pos= [0.5, -0.1, 0.0],rot=[0, 0, 1, 0], name="cube_obs") + + + + +@configclass +class FrankaCubeEnvCfg_PLAY(FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/mdp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/mdp/__init__.py new file mode 100755 index 000000000000..f53d3692aa6d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/mdp/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This sub-module contains the functions that are specific to the lift environments.""" + +from isaaclab.envs.mdp import * # noqa: F401, F403 + +from .observations import * # noqa: F401, F403 +from .rewards import * # noqa: F401, F403 +from .terminations import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/mdp/observations.py new file mode 100755 index 000000000000..32562680f03a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/mdp/observations.py @@ -0,0 +1,289 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import torch +from typing import TYPE_CHECKING + +from isaaclab.assets import RigidObject +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils.math import subtract_frame_transforms, combine_frame_transforms +from isaaclab.assets import Articulation, RigidObject, RigidObjectCollection +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import FrameTransformer +#from isaaclab.utils.logging_helper import LoggingHelper, ErrorType, LogType + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def object_position_in_robot_root_frame( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + # loghelper : LoggingHelper = LoggingHelper() +) -> torch.Tensor: + """The position of the object in the robot's root frame.""" + robot: RigidObject = env.scene[robot_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + object_pos_w = object.data.root_pos_w[:, :3] + object_pos_b, _ = subtract_frame_transforms( + robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], object_pos_w + ) + + return object_pos_b + +def obstacle_position_in_robot_root_frame( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_cfg: SceneEntityCfg = SceneEntityCfg("obstacle"), + # loghelper : LoggingHelper = LoggingHelper() +) -> torch.Tensor: + """The position of the object in the robot's root frame.""" + robot: RigidObject = env.scene[robot_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + object_pos_w = object.data.root_pos_w[:, :3] + object_pos_b, _ = subtract_frame_transforms( + robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], object_pos_w + ) + + return object_pos_b + + +def reach_object( + env: ManagerBasedRLEnv, + std: float = 0.05, + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), + # loghelper : LoggingHelper = LoggingHelper() +) -> torch.Tensor: + """Reward the agent for reaching the object using tanh-kernel.""" + # extract the used quantities (to enable type-hinting) + object: RigidObject = env.scene[object_cfg.name] + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + # Target object position: (num_envs, 3) + cube_pos_w = object.data.root_pos_w + # End-effector position: (num_envs, 3) + ee_w = ee_frame.data.target_pos_w[..., 0, :] + # print("ee pos : ", ee_w) + # Distance of the end-effector to the object: (num_envs,) + object_ee_distance = torch.norm(cube_pos_w - ee_w, dim=1) + #if object_ee_distance.item() < std : + # print(f"Observed Object Reached : {object_ee_distance.item()}") + # loghelper.logsubtask(LogType.APPR) + # if object_ee_distance[0] < std: + # print(f"Reached object, dist :{object_ee_distance.item()}") + return object_ee_distance < std + +def object_grasped( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg= SceneEntityCfg("robot"), + ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), + object_cfg: SceneEntityCfg= SceneEntityCfg("object"), + diff_threshold: float = 0.06, + gripper_open_val: torch.tensor = torch.tensor([0.04]), + gripper_threshold: float = 0.005, + # loghelper : LoggingHelper = LoggingHelper() +) -> torch.Tensor: + """Check if an object is grasped by the specified robot.""" + + robot: Articulation = env.scene[robot_cfg.name] + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + + object_pos = object.data.root_pos_w + end_effector_pos = ee_frame.data.target_pos_w[:, 0, :] + pose_diff = torch.linalg.vector_norm(object_pos - end_effector_pos, dim=1) + + grasped = torch.logical_and( + pose_diff < diff_threshold, + torch.abs(robot.data.joint_pos[:, -1] - gripper_open_val.to(env.device)) > gripper_threshold, + ) + grasped = torch.logical_and( + grasped, torch.abs(robot.data.joint_pos[:, -2] - gripper_open_val.to(env.device)) > gripper_threshold + ) + #if grasped[0]: + # print(f"Observed Object grasped : {grasped.item()}") + # loghelper.logsubtask(LogType.GRASP) + return grasped + +def object_released( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg, + ee_frame_cfg: SceneEntityCfg, + object_cfg: SceneEntityCfg, + diff_threshold: float = 0.06, + gripper_open_val: torch.tensor = torch.tensor([0.04]), + gripper_threshold: float = 0.035, + # loghelper : LoggingHelper = LoggingHelper() +) -> torch.Tensor: + """Check if an object is grasped by the specified robot.""" + + robot: Articulation = env.scene[robot_cfg.name] + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + + object_pos = object.data.root_pos_w + end_effector_pos = ee_frame.data.target_pos_w[:, 0, :] + pose_diff = torch.linalg.vector_norm(object_pos - end_effector_pos, dim=1) + + grasped = torch.logical_and( + pose_diff < diff_threshold, + torch.abs(robot.data.joint_pos[:, -1]) > gripper_threshold, + ) + grasped = torch.logical_and( + grasped, torch.abs(robot.data.joint_pos[:, -2] ) > gripper_threshold + ) + # if grasped[0]: + # print(f"Observed Object grasped : {grasped.item()}") + # loghelper.logsubtask(LogType.GRASP) + return grasped + +def is_object_lifted( + env: ManagerBasedRLEnv, + obj_cfg: SceneEntityCfg = SceneEntityCfg("object"), + threshold : float = 0.05, + #loghelper : LoggingHelper = LoggingHelper() +): + #return true when object z coord above a threshold value + object = env.scene[obj_cfg.name] + # if object.data.root_pos_w[:, 2].item() > threshold : + # print(f"Observed Object Lifted : {object.data.root_pos_w[:, 2].item()}") + # loghelper.logsubtask(LogType.LIFT) + + return object.data.root_pos_w[:, 2] > threshold + + +def ee_frame_pos(env: ManagerBasedRLEnv, ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame")) -> torch.Tensor: + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + ee_frame_pos = ee_frame.data.target_pos_w[:, 0, :] - env.scene.env_origins[:, 0:3] + + return ee_frame_pos + + +def ee_frame_quat(env: ManagerBasedRLEnv, ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame")) -> torch.Tensor: + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + ee_frame_quat = ee_frame.data.target_quat_w[:, 0, :] + + return ee_frame_quat + + +def gripper_pos(env: ManagerBasedRLEnv, robot_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + robot: Articulation = env.scene[robot_cfg.name] + finger_joint_1 = robot.data.joint_pos[:, -1].clone().unsqueeze(1) + finger_joint_2 = -1 * robot.data.joint_pos[:, -2].clone().unsqueeze(1) + + return torch.cat((finger_joint_1, finger_joint_2), dim=1) + +def object_near_goal( + env: ManagerBasedRLEnv, + command_name: str = "object_pose", + threshold: float = 0.05, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + # loghelper : LoggingHelper = LoggingHelper() +) -> torch.Tensor: + """Termination condition for the object reaching the goal position. + + Args: + env: The environment. + command_name: The name of the command that is used to control the object. + threshold: The threshold for the object to reach the goal position. Defaults to 0.02. + robot_cfg: The robot configuration. Defaults to SceneEntityCfg("robot"). + object_cfg: The object configuration. Defaults to SceneEntityCfg("object"). + + """ + robot: RigidObject = env.scene[robot_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + command = env.command_manager.get_command(command_name) + object_pos_w = object.data.root_pos_w[:, :3] + object_pos_b, _ = subtract_frame_transforms( + robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], object_pos_w + ) + des_pos_b = command[:, :3] + # print(f"desired position : {des_pos_b}, actual position : {object_pos_b}") + error = torch.norm(des_pos_b - object_pos_b, dim=1) + #if error torch.Tensor: + """Termination condition for the object reaching the goal position. + + Args: + env: The environment. + command_name: The name of the command that is used to control the object. + threshold: The threshold for the object to reach the goal position. Defaults to 0.02. + robot_cfg: The robot configuration. Defaults to SceneEntityCfg("robot"). + object_cfg: The object configuration. Defaults to SceneEntityCfg("object"). + + """ + robot: RigidObject = env.scene[robot_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + command = env.command_manager.get_command(command_name) + object_pos_w = object.data.root_pos_w[:, :3] + object_pos_b, _ = subtract_frame_transforms( + robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], object_pos_w + ) + des_pos_b = command[:, :3] + # print(f"desired position : {des_pos_b}, actual position : {object_pos_b}") + error = torch.norm(des_pos_b - object_pos_b, dim=1) + #print("norm position error : ", error) + return error + +def robot_pose( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot") +): + robot: Articulation = env.scene[robot_cfg.name] + # print(robot.data.joint_pos) + return torch.tensor([0.04]) + +def position_command_error( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + command_name: str = "object_pose", + # loghelper : LoggingHelper = LoggingHelper() +) -> torch.Tensor: + """The position of the object in the robot's root frame.""" + robot: RigidObject = env.scene[robot_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + command = env.command_manager.get_command(command_name) + object_pos_w = object.data.root_pos_w[:, :3] + object_pos_b, _ = subtract_frame_transforms( + robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], object_pos_w + ) + des_pos_b = command[:, :3] + # print(f"desired position : {des_pos_b}, actual position : {object_pos_b}") + error = torch.sub(des_pos_b, object_pos_b) + #print("position error : ", error) + return error + +def get_joint_pos(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """The joint positions of the asset. + + Note: Only the joints configured in :attr:`asset_cfg.joint_ids` will have their positions returned. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + #print("Joint positions: : ", asset.data.joint_pos[:, asset_cfg.joint_ids]) + return asset.data.joint_pos[:, asset_cfg.joint_ids] + +# def logstep( +# env: ManagerBasedRLEnv, +# # loghelper : LoggingHelper +# ): +# loghelper.logstep() +# return torch.tensor([0]) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/mdp/rewards.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/mdp/rewards.py new file mode 100755 index 000000000000..4436f10c7e48 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/mdp/rewards.py @@ -0,0 +1,73 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import torch +from typing import TYPE_CHECKING + +from isaaclab.assets import RigidObject +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import FrameTransformer +from isaaclab.utils.math import combine_frame_transforms +#from isaaclab.utils.logging_helper import LoggingHelper, ErrorType, LogType + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def object_is_lifted( + env: ManagerBasedRLEnv, minimal_height: float, object_cfg: SceneEntityCfg = SceneEntityCfg("object") +) -> torch.Tensor: + """Reward the agent for lifting the object above the minimal height.""" + object: RigidObject = env.scene[object_cfg.name] + + return torch.where(object.data.root_pos_w[:, 2] > minimal_height, 1.0, 0.0) + + +def object_ee_distance( + env: ManagerBasedRLEnv, + std: float, + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), +) -> torch.Tensor: + """Reward the agent for reaching the object using tanh-kernel.""" + # extract the used quantities (to enable type-hinting) + object: RigidObject = env.scene[object_cfg.name] + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + # Target object position: (num_envs, 3) + cube_pos_w = object.data.root_pos_w + # End-effector position: (num_envs, 3) + ee_w = ee_frame.data.target_pos_w[..., 0, :] + # Distance of the end-effector to the object: (num_envs,) + object_ee_distance = torch.norm(cube_pos_w - ee_w, dim=1) + + return 1 - torch.tanh(object_ee_distance / std) + + + +def object_goal_distance( + env: ManagerBasedRLEnv, + std: float, + minimal_height: float, + command_name: str, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), +) -> torch.Tensor: + """Reward the agent for tracking the goal pose using tanh-kernel.""" + # extract the used quantities (to enable type-hinting) + robot: RigidObject = env.scene[robot_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + command = env.command_manager.get_command(command_name) + # compute the desired position in the world frame + des_pos_b = command[:, :3] + des_pos_w, _ = combine_frame_transforms(robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], des_pos_b) + # distance of the end-effector to the object: (num_envs,) + distance = torch.norm(des_pos_w - object.data.root_pos_w[:, :3], dim=1) + # rewarded if the object is lifted above the threshold + #if (object.data.root_pos_w[:, 2] > minimal_height) * (1 - torch.tanh(distance / std)): + # print(f"object goal distance : {distance} ") + #print(f" Goal position reward : {(object.data.root_pos_w[:, 2] > minimal_height) * (1 - torch.tanh(distance / std))}") + return (object.data.root_pos_w[:, 2] > minimal_height) * (1 - torch.tanh(distance / std)) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/mdp/terminations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/mdp/terminations.py new file mode 100755 index 000000000000..007a82d6ca83 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/mdp/terminations.py @@ -0,0 +1,97 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Common functions that can be used to activate certain terminations for the lift task. + +The functions can be passed to the :class:`isaaclab.managers.TerminationTermCfg` object to enable +the termination introduced by the function. +""" + +from __future__ import annotations + +import torch +from typing import TYPE_CHECKING + +from isaaclab.assets import RigidObject, Articulation +from isaaclab.managers import SceneEntityCfg + +from isaaclab.sensors import FrameTransformer +from isaaclab.utils.math import combine_frame_transforms, subtract_frame_transforms +#from isaaclab.utils.logging_helper import LoggingHelper, ErrorType, LogType +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def object_reached_goal( + env: ManagerBasedRLEnv, + command_name: str = "object_pose", + threshold: float = 0.02, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + # loghelper : LoggingHelper = LoggingHelper() +) -> torch.Tensor: + """Termination condition for the object reaching the goal position. + + Args: + env: The environment. + command_name: The name of the command that is used to control the object. + threshold: The threshold for the object to reach the goal position. Defaults to 0.02. + robot_cfg: The robot configuration. Defaults to SceneEntityCfg("robot"). + object_cfg: The object configuration. Defaults to SceneEntityCfg("object"). + + """ + # extract the used quantities (to enable type-hinting) + robot: RigidObject = env.scene[robot_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + command = env.command_manager.get_command(command_name) + # compute the desired position in the world frame + des_pos_b = command[:, :3] + des_pos_w, _ = combine_frame_transforms(robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], des_pos_b) + # distance of the end-effector to the object: (num_envs,) + distance = torch.norm(des_pos_w - object.data.root_pos_w[:, :3], dim=1) + # print(f"For DEBUG : DISTANCE TO GOAL : {distance}") + # if(distance.item() < threshold): + # loghelper.logsubtask(LogType.FINISH) + test = distance < threshold + if (torch.any(test)): + print("debug : terminations : ", torch.any(test)) + return distance < threshold + + +def placed( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + ee_frame_cfg: SceneEntityCfg=SceneEntityCfg("ee_frame"), + command_name: str = "object_pose", + threshold: torch.tensor = torch.tensor([0.05], device="cuda:0"), + gripper_open_val= torch.tensor([0.038], device="cuda:0"), #open + atol=0.0001, + rtol=0.0001, + gripper_threshold: float = 0.005, +): + robot: Articulation = env.scene[robot_cfg.name] + + command = env.command_manager.get_command(command_name) + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name], + object: RigidObject = env.scene[object_cfg.name] + + des_pos_b = command[:, :3] + des_pos_w, _ = combine_frame_transforms(robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], des_pos_b) + # distance of the end-effector to the object: (num_envs,) + + object_pos = object.data.root_pos_w + #end_effector_pos = ee_frame.data.target_pos_w[:, 0, :] + # pose_diff = torch.linalg.vector_norm(object_pos - end_effector_pos, dim=1) + distance = torch.norm(des_pos_w - object_pos[:, :3], dim=1) + object_position = distance gripper_open_val) + gripper_open = robot.data.joint_pos[:, -1] > gripper_open_val + state = torch.logical_and(object_position, gripper_open) + return state \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/pour_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/pour_env_cfg.py new file mode 100755 index 000000000000..c1a4dbfedb50 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/pour_env_cfg.py @@ -0,0 +1,361 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING +from enum import Enum +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, DeformableObjectCfg, RigidObjectCfg, RigidObjectCollection, RigidObjectCollectionCfg +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import CurriculumTermCfg as CurrTerm +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import FrameTransformerCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +import warp as wp +from . import mdp +#from isaaclab.utils.logging_helper import LoggingHelper, ErrorType, LogType + + + + + +## +# Scene definition +## + + +@configclass +class ObjectTableSceneCfg(InteractiveSceneCfg): + """Configuration for the lift scene with a robot and a object. + This is the abstract base implementation, the exact scene is defined in the derived classes + which need to set the target object, robot and end-effector frames + """ + + # robots: will be populated by agent env cfg + robot: ArticulationCfg = MISSING + # end-effector sensor: will be populated by agent env cfg + ee_frame: FrameTransformerCfg = MISSING + # target object: will be populated by agent env cfg + object: RigidObjectCfg | DeformableObjectCfg = MISSING + + #for collision + # obstacle: RigidObjectCfg =MISSING + + + # glassware = RigidObjectCollectionCfg(rigid_objects={"vial" : vial, "flask" : flask}) + + + # Table + table = AssetBaseCfg( + prim_path="{ENV_REGEX_NS}/Table", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.5, 0, 0], rot=[0.707, 0, 0, 0.707]), + spawn=UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd"), + ) + + # plane + plane = AssetBaseCfg( + prim_path="/World/GroundPlane", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0, 0, -1.05]), + spawn=GroundPlaneCfg(), + ) + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + + + + +## +# MDP settings +## + + +@configclass +class CommandsCfg: + """Command terms for the MDP.""" +#### Goal Positions + object_pose = mdp.UniformPoseCommandCfg( + asset_name="robot", + body_name=MISSING, # will be set by agent env cfg + resampling_time_range=(10.0, 10.0), + debug_vis=True, + ranges=mdp.UniformPoseCommandCfg.Ranges( + # pos_x=(0.3,0.3), pos_y=(-0.28, -0.28), pos_z=(0.1, 0.1), roll=(0.0, 0.0), pitch=(0.0, 0.0), yaw=(0.0, 0.0) + pos_x=(0.4,0.4), pos_y=(-0.3, -0.3), pos_z=(0.08, 0.08), roll=(0.0, 0.0), pitch=(0.0, 0.0), yaw=(0.0, 0.0) + ), + ) + + midpoint_pose = mdp.UniformPoseCommandCfg( + asset_name="robot", + body_name="panda_hand", + resampling_time_range=(10.0, 10.0), + debug_vis=True, + ranges=mdp.UniformPoseCommandCfg.Ranges( + # pos_x=(0.3,0.3), pos_y=(-0.28, -0.28), pos_z=(0.1, 0.1), roll=(0.0, 0.0), pitch=(0.0, 0.0), yaw=(0.0, 0.0) + pos_x=(0,0), pos_y=(-0.25, -0.25), pos_z=(0.4, 0.4), roll=(0.0, 0.0), pitch=(0.0, 0.0), yaw=(0.0, 0.0) + ), + ) + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + # will be set by agent env cfg + arm_action: mdp.JointPositionActionCfg | mdp.DifferentialInverseKinematicsActionCfg = MISSING + gripper_action: mdp.BinaryJointPositionActionCfg = MISSING + + +@configclass +class ObservationsCfg(): + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + # def set_loghelper(self, loghelper: LoggingHelper): + # self.logstep.params["loghelper"] = loghelper + # logstep = ObsTerm(func = mdp.logstep) + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + object_position = ObsTerm(func=mdp.object_position_in_robot_root_frame) + target_object_position = ObsTerm(func=mdp.generated_commands, params={"command_name": "object_pose"}) + actions = ObsTerm(func=mdp.last_action) + # object_to_target = ObsTerm(func=mdp.object_near_goal) + eef_pos = ObsTerm(func=mdp.ee_frame_pos) + eef_quat = ObsTerm(func=mdp.ee_frame_quat) + gripper_pos = ObsTerm(func=mdp.gripper_pos) + #obstacle_pos = ObsTerm(func=mdp.obstacle_position_in_robot_root_frame) + #robot_pose=ObsTerm(func=mdp.robot_pose) + #object_to_target = ObsTerm(func=mdp.position_command_error) + #norm_error = ObsTerm(func=mdp.object_goal_norm_error) + abs_joint_pos = ObsTerm(func=mdp.get_joint_pos) + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class SubtaskCfg(ObsGroup): + """Observations for subtask group.""" + # def set_loghelper(self, loghelper: LoggingHelper): + # # Inject the logger into any terms that need it + # self.appr.params["loghelper"] = loghelper + # self.grasp.params["loghelper"] = loghelper + # self.lift.params["loghelper"] = loghelper + # self.appr_goal.params["loghelper"] = loghelper + #self.loghelper = loghelper + + + # appr = ObsTerm( + # func=mdp.reach_object, + # params={ + # "ee_frame_cfg": SceneEntityCfg("ee_frame"), + # "object_cfg": SceneEntityCfg("object"), + # "std" : 0.05 + # } + # ) + + grasp = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("object"), + }, + ) + # lift = ObsTerm( + # func=mdp.is_object_lifted, + # params={ + # "obj_cfg": SceneEntityCfg("object"), + # "threshold" : 0.1 + # } + # ) + appr_goal = ObsTerm( + func=mdp.object_near_goal, + params={ + "threshold": 0.05, + "command_name": "object_pose", + }, + ) + # release_object = ObsTerm( + # func=mdp.object_released, + # params={ + # "robot_cfg": SceneEntityCfg("robot"), + # "ee_frame_cfg": SceneEntityCfg("ee_frame"), + # "object_cfg": SceneEntityCfg("object"), + # }, + # ) + + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + + + + # observation groups + policy: PolicyCfg = PolicyCfg() + subtask_terms : SubtaskCfg = SubtaskCfg() + +@configclass +class EventCfg: + """Configuration for events.""" + ## add in log helper funcs + # def set_loghelper(self, loghelper: LoggingHelper): + # self.reset_all.params["loghelper"] = loghelper + reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset") + + reset_object_position = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": {"x": (0, 0.2), "y": (0, 0.25), "z": (0.05, 0.1)}, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("object", body_names="Object"), + }, + ) + #randomises the scale of the object + randomise_object_scale = EventTerm( + func=mdp.randomize_rigid_body_scale, + mode="prestartup", + params={ + "scale_range": {"x": (0.4, 0.7), "y": (0.4, 0.7), "z": (0.5, 0.7)}, + "asset_cfg": SceneEntityCfg("object", body_names="Object"), + }, + # params={ + # "scale_range": {"x": (0.5, 0.5), "y": (0.5, 0.5), "z": (0.6, 0.7)}, + # "asset_cfg": SceneEntityCfg("object", body_names="Object"), + # }, + ) + + + +@configclass +class RewardsCfg: + """Reward terms for the MDP.""" + + reaching_object = RewTerm(func=mdp.object_ee_distance, params={"std": 0.1}, weight=1.0) + + lifting_object = RewTerm(func=mdp.object_is_lifted, params={"minimal_height": 0.14}, weight=30.0) + + reaching_target = RewTerm(func=mdp.object_goal_norm_error, weight = 20.0) + + object_goal_tracking = RewTerm( + func=mdp.object_goal_distance, + params={"std": 0.3, "minimal_height": 0.04, "command_name": "object_pose"}, + weight=16.0, + ) + + object_goal_tracking_fine_grained = RewTerm( + func=mdp.object_goal_distance, + params={"std": 0.05, "minimal_height": 0.04, "command_name": "object_pose"}, + weight=5.0, + ) + + # action penalty + action_rate = RewTerm(func=mdp.action_rate_l2, weight=-1e-4) + + joint_vel = RewTerm( + func=mdp.joint_vel_l2, + weight=-1e-4, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + # def set_loghelper(self, loghelper: LoggingHelper): + # self.success.params["loghelper"] = loghelper + # self.time_out.params["loghelper"] = loghelper + # self.object_dropping.params["loghelper"] = loghelper + # # self.loghelper = loghelper + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + #joint_violation = DoneTerm(func=mdp.joint_pos_out_of_limit) + + #object_orientation = DoneTerm(func=mdp.bad_orientation) + + #joint_effort = DoneTerm(func=mdp.joint_effort_out_of_limit) + + object_dropping = DoneTerm( + func=mdp.root_height_below_minimum, params={"minimum_height": -0.05, "asset_cfg": SceneEntityCfg("object")} + ) + #fix object dropping + success = DoneTerm(func=mdp.object_near_goal) + + +@configclass +class CurriculumCfg: + """Curriculum terms for the MDP.""" + + action_rate = CurrTerm( + func=mdp.modify_reward_weight, params={"term_name": "action_rate", "weight": -1e-1, "num_steps": 10000} + ) + + joint_vel = CurrTerm( + func=mdp.modify_reward_weight, params={"term_name": "joint_vel", "weight": -1e-1, "num_steps": 10000} + ) + + +## +# Environment configuration +## + + + +@configclass +class CubeEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the lifting environment.""" + # loghelper = LoggingHelper() + # Scene settings + scene: ObjectTableSceneCfg = ObjectTableSceneCfg(num_envs=4096, env_spacing=2.5, replicate_physics=False) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + commands: CommandsCfg = CommandsCfg() + # MDP settings + rewards: RewardsCfg = RewardsCfg() + terminations: TerminationsCfg = TerminationsCfg() + events: EventCfg = EventCfg() + curriculum: CurriculumCfg = CurriculumCfg() + + #subtask_statemachine: SubtaskStateMachine = SubtaskStateMachine() + + def __post_init__(self): + """Post initialization.""" + print("debug cfg : ", self.observations.policy.actions) + # general settings + + self.decimation = 2 + self.episode_length_s = 60 + # simulation settings + self.sim.dt = 0.01 # 100Hz + self.sim.render_interval = self.decimation + self.viewer.eye = (1.0, 1.0, 1.0) + self.viewer.lookat = (0.0, 0.0, -0.1) + # self.terminations.set_loghelper(self.loghelper) + #self.events.set_loghelper(self.loghelper) + # self.observations.policy.set_loghelper(self.loghelper) + # self.observations.subtask_terms.set_loghelper(self.loghelper) + self.sim.physx.bounce_threshold_velocity = 0.2 + self.sim.physx.bounce_threshold_velocity = 0.01 + # self.sim.physx.gpu_found_lost_aggregate_pairs_capacity = 1024 * 1024 * 4 + # self.sim.physx.gpu_total_aggregate_pairs_capacity = 16 * 1024 + self.sim.physx.friction_correlation_distance = 0.00625 \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/__init__.py new file mode 100755 index 000000000000..fd33aacd3a5b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configurations for the object lift environments.""" + +# We leave this file empty since we don't want to expose any configs in this package directly. +# We still need this file to import the "config" module in the parent package. diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/__init__.py new file mode 100755 index 000000000000..fd33aacd3a5b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configurations for the object lift environments.""" + +# We leave this file empty since we don't want to expose any configs in this package directly. +# We still need this file to import the "config" module in the parent package. diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py new file mode 100755 index 000000000000..f290d45c4b1a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py @@ -0,0 +1,141 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +import gymnasium as gym +import os + +from . import agents + +## +# Register Gym environments. +## + +## +# Joint Position Control +## + +gym.register( + id="Isaac-Cube-Franka-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:FrankaCubeEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:LiftCubePPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "sb3_cfg_entry_point": f"{agents.__name__}:sb3_ppo_cfg.yaml", + }, + disable_env_checker=True, +) +gym.register( + id="Dev-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.dev_env_cfg:FrankaDevEnvCfg", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:LiftCubePPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "sb3_cfg_entry_point": f"{agents.__name__}:sb3_ppo_cfg.yaml", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Cube-Franka-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.joint_pos_env_cfg:FrankaCubeEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:LiftCubePPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "sb3_cfg_entry_point": f"{agents.__name__}:sb3_ppo_cfg.yaml", + }, + disable_env_checker=True, +) + +gym.register( + id="Dev-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.dev_env_cfg:FrankaDevEnvCfg_PLAY", + "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:LiftCubePPORunnerCfg", + "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", + "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", + "sb3_cfg_entry_point": f"{agents.__name__}:sb3_ppo_cfg.yaml", + }, + disable_env_checker=True, +) + +## +# Inverse Kinematics - Absolute Pose Control +## + +gym.register( + id="Isaac-Cube-Franka-IK-Abs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.ik_abs_env_cfg:FrankaCubeEnvCfg", + }, + disable_env_checker=True, +) + +# gym.register( +# id="Isaac-Lift-Teddy-Bear-Franka-IK-Abs-v0", +# entry_point="isaaclab.envs:ManagerBasedRLEnv", +# kwargs={ +# "env_cfg_entry_point": f"{__name__}.ik_abs_env_cfg:FrankaTeddyBearLiftEnvCfg", +# }, +# disable_env_checker=True, +# ) + +## +# Inverse Kinematics - Relative Pose Control +## + +gym.register( + id="Isaac-Cube-Franka-IK-Rel-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.ik_rel_env_cfg:FrankaCubeEnvCfg", + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), + }, + disable_env_checker=True, +) + +gym.register( + id="Dev-IK-Abs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.dev_env_cfg:FrankaDevEnvCfg", + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), + }, + disable_env_checker=True, +) + +gym.register( + id="Dev-IK-Rel-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.dev_ik_rel_env_cfg:FrankaDevEnvCfg", + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), + "robomimic_bc_trans_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_trans.json"), + "robomimic_hbc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/hbc.json"), + "robomimic_bcq_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bcq.json"), + #"robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc.json"), + }, + disable_env_checker=True, +) +gym.register( + id="Dev-IK-Rel-v1", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.dev_ik_rel_env_obs:FrankaDevEnvCfg", + "robomimic_bc_rnn_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), + "robomimic_bc_trans_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_trans.json"), + "robomimic_hbc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/hbc.json"), + "robomimic_bcq_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bcq.json"), + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc.json"), + }, + disable_env_checker=True, +) + diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/__init__.py new file mode 100755 index 000000000000..e75ca2bc3f90 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/rl_games_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/rl_games_ppo_cfg.yaml new file mode 100755 index 000000000000..6ed68d88912c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/rl_games_ppo_cfg.yaml @@ -0,0 +1,79 @@ +params: + seed: 42 + + # environment wrapper clipping + env: + clip_observations: 100.0 + clip_actions: 100.0 + + algo: + name: a2c_continuous + + model: + name: continuous_a2c_logstd + + network: + name: actor_critic + separate: False + space: + continuous: + mu_activation: None + sigma_activation: None + + mu_init: + name: default + sigma_init: + name: const_initializer + val: 0 + fixed_sigma: True + mlp: + units: [256, 128, 64] + activation: elu + d2rl: False + + initializer: + name: default + regularizer: + name: None + + load_checkpoint: False # flag which sets whether to load the checkpoint + load_path: '' # path to the checkpoint to load + + config: + name: franka_lift + env_name: rlgpu + device: 'cuda:0' + device_name: 'cuda:0' + multi_gpu: False + ppo: True + mixed_precision: False + normalize_input: True + normalize_value: True + value_bootstrap: False + num_actors: -1 + reward_shaper: + scale_value: 0.01 + normalize_advantage: True + gamma: 0.99 + tau: 0.95 + learning_rate: 1e-4 + lr_schedule: adaptive + schedule_type: legacy + kl_threshold: 0.01 + score_to_win: 100000000 + max_epochs: 1500 + save_best_after: 100 + save_frequency: 50 + print_stats: True + grad_norm: 1.0 + entropy_coef: 0.001 + truncate_grads: True + e_clip: 0.2 + horizon_length: 24 + minibatch_size: 24576 + mini_epochs: 8 + critic_coef: 4 + clip_value: True + clip_actions: False + seq_len: 4 + bounds_loss_coef: 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc.json new file mode 100755 index 000000000000..3108d71b52d3 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc.json @@ -0,0 +1,135 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_202507210930", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "pick-place-tabletop" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 100, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": true, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": null, + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "terminate_on_success" : true, + "n": 50, + "horizon": 400, + "rate": 50 + } + }, + "train": { + "data": null, + "output_dir": "/training_runs/bc", + "num_data_workers": 1, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_normalize_obs": false, + "hdf5_filter_key": "train", + "hdf5_validation_filter_key": "valid", + "seq_length": 10, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 3000, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.01, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gmm": { + "enabled": true, + "num_modes": 10, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false + }, + "rnn": { + "enabled": false, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object_position", + "target_object_position", + "joint_pos", + "joint_vel", + "actions" + ], + "rgb": [], + "depth": [], + "scan": [] + } + } + } +} \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json new file mode 100755 index 000000000000..6b09227a280f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json @@ -0,0 +1,135 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_new_demos", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "Lift_beaker_new_demos" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": null, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": true, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": "Dev-IK-Rel-v0", + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "terminate_on_success" : true, + "n": 50, + "horizon": 400, + "rate": 50 + } + }, + "train": { + "data": null, + "output_dir": "/training_runs/bc_rnn", + "num_data_workers": 1, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_normalize_obs": false, + "hdf5_filter_key": "train", + "hdf5_validation_filter_key": "valid", + "seq_length": 10, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.01, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gmm": { + "enabled": true, + "num_modes": 10, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object_position", + "target_object_position", + "joint_pos", + "joint_vel", + "actions" + ], + "rgb": [], + "depth": [], + "scan": [] + } + } + } +} \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim_not.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim_not.json new file mode 100755 index 000000000000..00d956d4cf16 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim_not.json @@ -0,0 +1,306 @@ +{ + "algo_name": "hbc", + "experiment": { + "name": "hbc_202507211400", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "pick-place-tabletop" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 50, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": false + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": "Dev-IK-Rel-v0", + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": null, + "output_dir": "../hbc_trained_models", + "num_data_workers": 0, + "hdf5_cache_mode": "all", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": true, + "hdf5_normalize_obs": false, + "hdf5_filter_key": "train", + "hdf5_validation_filter_key": "valid", + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 1 + }, + "algo": { + "mode": "separate", + "actor_use_random_subgoals": false, + "subgoal_update_interval": 10, + "latent_subgoal": { + "enabled": false, + "prior_correction": { + "enabled": false, + "num_samples": 100 + } + }, + "planner": { + "optim_params": { + "goal_network": { + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.1, + "epoch_schedule": [] + }, + "regularization": { + "L2": 0.0 + } + } + }, + "subgoal_horizon": 10, + "ae": { + "planner_layer_dims": [ + 300, + 400 + ] + }, + "vae": { + "enabled": true, + "latent_dim": 16, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + 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a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/hbc.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/hbc.json new file mode 100755 index 000000000000..e4be27fbc254 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/hbc.json @@ -0,0 +1,306 @@ +{ + "algo_name": "hbc", + "experiment": { + "name": "hbc_202507211400", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "pick-place-tabletop" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 50, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": false, + "on_best_rollout_success_rate"` : false + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": "Dev-IK-Rel-v0", + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": null, + "output_dir": "../hbc_trained_models", + "num_data_workers": 0, + "hdf5_cache_mode": "all", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": true, + "hdf5_normalize_obs": false, + "hdf5_filter_key": "train", + "hdf5_validation_filter_key": "valid", + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 1 + }, + "algo": { + "mode": "separate", + "actor_use_random_subgoals": false, + "subgoal_update_interval": 10, + "latent_subgoal": { + "enabled": false, + "prior_correction": { + "enabled": false, + "num_samples": 100 + } + }, + "planner": { + "optim_params": { + "goal_network": { + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.1, + "epoch_schedule": [] + }, + "regularization": { + "L2": 0.0 + } + } + }, + "subgoal_horizon": 10, + "ae": { + "planner_layer_dims": [ + 300, + 400 + ] + }, + "vae": { + "enabled": true, + "latent_dim": 16, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + } + }, + "actor": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.1, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [ + 1024, + 1024 + ], + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + } + }, + "observation": { + "planner": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object_position", + "target_object_position", + + "joint_pos", + "joint_vel", + "actions" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + }, + "subgoal": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object_position", + "target_object_position", + "joint_pos", + "joint_vel", + "actions" + ], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + }, + "actor": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object_position", + "target_object_position", + "joint_pos", + "joint_vel", + "actions" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + } + }, + "meta": { + "hp_base_config_file": null, + "hp_keys": [], + "hp_values": [] + } +} \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/rsl_rl_ppo_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/rsl_rl_ppo_cfg.py new file mode 100755 index 000000000000..e3fb13e1d1f7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/rsl_rl_ppo_cfg.py @@ -0,0 +1,37 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.utils import configclass + +from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg + + +@configclass +class LiftCubePPORunnerCfg(RslRlOnPolicyRunnerCfg): + num_steps_per_env = 24 + max_iterations = 1500 + save_interval = 50 + experiment_name = "franka_lift" + empirical_normalization = False + policy = RslRlPpoActorCriticCfg( + init_noise_std=1.0, + actor_hidden_dims=[256, 128, 64], + critic_hidden_dims=[256, 128, 64], + activation="elu", + ) + algorithm = RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.006, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1.0e-4, + schedule="adaptive", + gamma=0.98, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/sb3_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/sb3_ppo_cfg.yaml new file mode 100755 index 000000000000..6d6f15781acc --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/sb3_ppo_cfg.yaml @@ -0,0 +1,28 @@ +# Reference: https://github.com/DLR-RM/rl-baselines3-zoo/blob/master/hyperparams/ppo.yml#L32 +seed: 42 + +# epoch * n_steps * nenvs: 500×512*8*8 +n_timesteps: 16384000 +policy: 'MlpPolicy' +n_steps: 64 +# mini batch size: num_envs * nsteps / nminibatches 2048×512÷2048 +batch_size: 192 +gae_lambda: 0.95 +gamma: 0.99 +n_epochs: 8 +ent_coef: 0.00 +vf_coef: 0.0001 +learning_rate: !!float 3e-4 +clip_range: 0.2 +policy_kwargs: "dict( + activation_fn=nn.ELU, + net_arch=dict(pi=[256, 128, 64], vf=[256, 128, 64]) + )" +target_kl: 0.01 +max_grad_norm: 1.0 + +# # Uses VecNormalize class to normalize obs +# normalize_input: True +# # Uses VecNormalize class to normalize rew +# normalize_value: True +# clip_obs: 5 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/skrl_ppo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/skrl_ppo_cfg.yaml new file mode 100755 index 000000000000..94337d46bac6 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/skrl_ppo_cfg.yaml @@ -0,0 +1,80 @@ +seed: 42 + + +# Models are instantiated using skrl's model instantiator utility +# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html +models: + separate: False + policy: # see gaussian_model parameters + class: GaussianMixin + clip_actions: False + clip_log_std: True + min_log_std: -20.0 + max_log_std: 2.0 + initial_log_std: 0.0 + network: + - name: net + input: STATES + layers: [256, 128, 64] + activations: elu + output: ACTIONS + value: # see deterministic_model parameters + class: DeterministicMixin + clip_actions: False + network: + - name: net + input: STATES + layers: [256, 128, 64] + activations: elu + output: ONE + + +# Rollout memory +# https://skrl.readthedocs.io/en/latest/api/memories/random.html +memory: + class: RandomMemory + memory_size: -1 # automatically determined (same as agent:rollouts) + + +# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG) +# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html +agent: + class: PPO + rollouts: 24 + learning_epochs: 8 + mini_batches: 4 + discount_factor: 0.99 + lambda: 0.95 + learning_rate: 1.0e-04 + learning_rate_scheduler: KLAdaptiveLR + learning_rate_scheduler_kwargs: + kl_threshold: 0.01 + state_preprocessor: RunningStandardScaler + state_preprocessor_kwargs: null + value_preprocessor: RunningStandardScaler + value_preprocessor_kwargs: null + random_timesteps: 0 + learning_starts: 0 + grad_norm_clip: 1.0 + ratio_clip: 0.2 + value_clip: 0.2 + clip_predicted_values: True + entropy_loss_scale: 0.001 + value_loss_scale: 2.0 + kl_threshold: 0.0 + rewards_shaper_scale: 0.01 + time_limit_bootstrap: False + # logging and checkpoint + experiment: + directory: "franka_lift" + experiment_name: "" + write_interval: auto + checkpoint_interval: auto + + +# Sequential trainer +# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html +trainer: + class: SequentialTrainer + timesteps: 36000 + environment_info: log diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_env_cfg.py new file mode 100755 index 000000000000..bdf7eb7a81cc --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_env_cfg.py @@ -0,0 +1,132 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.assets import RigidObjectCfg, ArticulationCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab_tasks.manager_based.manipulation.cube_lift import mdp +from isaaclab_tasks.manager_based.manipulation.cube_lift.lift_env_cfg import CubeEnvCfg +#from isaaclab_assets.glassware.glassware_objects import Chem_Assets + + +import math +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + +@configclass +class FrankaDevEnvCfg(CubeEnvCfg): + def __post_init__(self): + # post init of parent + + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace( + prim_path="{ENV_REGEX_NS}/Robot", + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + + "panda_joint1": 0.3281, + "panda_joint2": 0.3684, + "panda_joint3": -0.2787, + "panda_joint4": -2.6138, + "panda_joint5": -2.7, + "panda_joint6": 2.4991, # +90° → keeps hand level + "panda_joint7": 0.3331, + "panda_finger_joint1": 0.04, # open gripper + "panda_finger_joint2": 0.04, + } + ), + ) + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls"), + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + + self.actions.gripper_action = mdp.BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["panda_finger.*"], + open_command_expr={"panda_finger_.*": 0.04}, + close_command_expr={"panda_finger_.*": 0.0}, + ) + # Set the body name for the end effector + self.commands.object_pose.body_name = "panda_hand" + self.commands.object_pose.body_name = "panda_hand" + + # glassware = Chem_Assets() + # Set each stacking cube deterministically + #self.scene.object = glassware.beaker(pos=[0.5, 0.0, 0.01],name="Object") + #### everything else leave default + # self.scene.flask = glassware.flask() + # self.scene.vial = glassware.vial() + # self.scene.beaker = glassware.beaker() + #self.scene.stirplate = glassware.stirplate() + #self.scene.random = glassware.random_object() + # self.scene.obstacle = glassware.cube_obs(pos= [0.8, -0.8, 0.0],rot=[0, 0, 1, 0], name="cube_obs") + #self.scene.obstacle = glassware.stirplate(pos= [0.5, -0.32, 0.0],rot=[0.707, 0, 0, -0.707], name="cube_obs") + # self.scene.obstacle = glassware.stirplate(pos= [0.8, -0.8, 0.0], rot=[0.707, 0, 0, -0.707], name="cube_obs") + self.scene.object = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Object", + init_state=RigidObjectCfg.InitialStateCfg( pos=[0.5, 0.0, 0.02],rot=[0, 0, 1, 0]), + spawn=UsdFileCfg( + usd_path=f"source/isaaclab_assets/isaaclab_assets/glassware/beaker/beaker.usd", + scale=(0.5, 0.5, 0.5), + rigid_props=RigidBodyPropertiesCfg( + solver_position_iteration_count=16, + solver_velocity_iteration_count=1, + max_angular_velocity=1000.0, + max_linear_velocity=1000.0, + max_depenetration_velocity=5.0, + disable_gravity=False, + ), + ), + ) + # Listens to the required transforms + marker_cfg = FRAME_MARKER_CFG.copy() + marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + marker_cfg.prim_path = "/Visuals/FrameTransformer" + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_link0", + debug_vis=False, + visualizer_cfg=marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_hand", + name="end_effector", + offset=OffsetCfg( + pos=[0.0, 0.0, 0.1034], + + ), + ), + ], + ) + + +@configclass +class FrankaDevEnvCfg_PLAY(FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_cfg.py new file mode 100755 index 000000000000..929077ee9a67 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_cfg.py @@ -0,0 +1,63 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass +from isaaclab.assets import RigidObjectCfg, ArticulationCfg + +from . import dev_env_cfg +import math +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + + +@configclass +class FrankaDevEnvCfg(dev_env_cfg.FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace( + prim_path="{ENV_REGEX_NS}/Robot", + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + + "panda_joint1": 0.3281, + "panda_joint2": -0.3684, + "panda_joint3": -0.2787, + "panda_joint4": -2.6138, + "panda_joint5": -2.7527, + "panda_joint6": 2.4991, # +90° → keeps hand level + "panda_joint7": 0.3331, + "panda_finger_joint1": 0.04, # open gripper + "panda_finger_joint2": 0.04, + } + ), + ) + # replace with relative position controller + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + ) + + + +@configclass +class FrankaCubeEnvCfg_PLAY(FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_obs.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_obs.py new file mode 100755 index 000000000000..4332e690b196 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_obs.py @@ -0,0 +1,49 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass +from isaaclab.assets import RigidObjectCfg + +from . import dev_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip +#from isaaclab_assets.glassware.glassware_objects import Chem_Assets + +@configclass +class FrankaDevEnvCfg(dev_env_cfg.FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # replace with relative position controller + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + # glassware = Chem_Assets() + #self.scene.obstacle = glassware.cube_obs(pos= [0.5, -0.1, 0.0],rot=[0, 0, 1, 0], name="cube_obs") + + + + +@configclass +class FrankaCubeEnvCfg_PLAY(FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_osc_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_osc_cfg.py new file mode 100755 index 000000000000..bb1d4bd550e2 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_osc_cfg.py @@ -0,0 +1,114 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.assets import RigidObjectCfg, ArticulationCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab_tasks.manager_based.manipulation.cube_lift import mdp +from isaaclab_tasks.manager_based.manipulation.cube_lift.lift_env_cfg import CubeEnvCfg +from isaaclab_assets.glassware.glassware_objects import Chem_Assets + + +import math +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + +@configclass +class FrankaDevEnvCfg(CubeEnvCfg): + def __post_init__(self): + # post init of parent + + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace( + prim_path="{ENV_REGEX_NS}/Robot", + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + + "panda_joint1": 0.405, + "panda_joint2": 0.35, + "panda_joint3": -0.22, + "panda_joint4": -3.0, + "panda_joint5": -2.85, + "panda_joint6": math.pi / 2, # +90° → keeps hand level + "panda_joint7": 0.9, + "panda_finger_joint1": 0.04, # open gripper + "panda_finger_joint2": 0.04, + } + ), + ) + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls"), + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + + self.actions.gripper_action = mdp.BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["panda_finger.*"], + open_command_expr={"panda_finger_.*": 0.04}, + close_command_expr={"panda_finger_.*": 0.0}, + ) + # Set the body name for the end effector + self.commands.object_pose.body_name = "panda_hand" + self.commands.object_pose.body_name = "panda_hand" + + glassware = Chem_Assets() + # Set each stacking cube deterministically + self.scene.object = glassware.beaker(pos=[0.4, 0.0, 0.0203],name="Object") + #### everything else leave default + # self.scene.flask = glassware.flask() + # self.scene.vial = glassware.vial() + # self.scene.beaker = glassware.beaker() + self.scene.stirplate = glassware.stirplate() + #self.scene.random = glassware.random_object() + + # Listens to the required transforms + marker_cfg = FRAME_MARKER_CFG.copy() + marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + marker_cfg.prim_path = "/Visuals/FrameTransformer" + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_link0", + debug_vis=False, + visualizer_cfg=marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_hand", + name="end_effector", + offset=OffsetCfg( + pos=[0.0, 0.0, 0.1034], + + ), + ), + ], + ) + + +@configclass +class FrankaDevEnvCfg_PLAY(FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/ik_abs_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/ik_abs_env_cfg.py new file mode 100755 index 000000000000..255b5b55eb86 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/ik_abs_env_cfg.py @@ -0,0 +1,128 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.assets import DeformableObjectCfg +from isaaclab.assets import RigidObjectCfg, ArticulationCfg +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sim.spawners import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR + +import isaaclab_tasks.manager_based.manipulation.cube_lift.mdp as mdp + +from . import joint_pos_env_cfg +import math +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +## +# Rigid object lift environment. +## + + +@configclass +class FrankaCubeEnvCfg(joint_pos_env_cfg.FrankaCubeEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace( + prim_path="{ENV_REGEX_NS}/Robot", + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + + "panda_joint1": 0.405, + "panda_joint2": 0.35, + "panda_joint3": -0.22, + "panda_joint4": -3.0, + "panda_joint5": -2.85, + "panda_joint6": math.pi / 2, # +90° → keeps hand level + "panda_joint7": 0.9, + "panda_finger_joint1": 0.04, # open gripper + "panda_finger_joint2": 0.04, + } + ), + ) + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls"), + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + print("action shape: ", self.actions.arm_action.shape) + + +@configclass +class FrankaCubeEnvCfg_PLAY(FrankaCubeEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False + + +## +# Deformable object lift environment. +## + + +@configclass +class FrankaTeddyBearLiftEnvCfg(FrankaCubeLiftEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + self.scene.object = DeformableObjectCfg( + prim_path="{ENV_REGEX_NS}/Object", + init_state=DeformableObjectCfg.InitialStateCfg(pos=(0.5, 0, 0.05), rot=(0.707, 0, 0, 0.707)), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Objects/Teddy_Bear/teddy_bear.usd", + scale=(0.01, 0.01, 0.01), + ), + ) + + # Make the end effector less stiff to not hurt the poor teddy bear + self.scene.robot.actuators["panda_hand"].effort_limit = 50.0 + self.scene.robot.actuators["panda_hand"].stiffness = 40.0 + self.scene.robot.actuators["panda_hand"].damping = 10.0 + + # Disable replicate physics as it doesn't work for deformable objects + # FIXME: This should be fixed by the PhysX replication system. + self.scene.replicate_physics = False + + # Set events for the specific object type (deformable cube) + self.events.reset_object_position = EventTerm( + func=mdp.reset_nodal_state_uniform, + mode="reset", + params={ + "position_range": {"x": (-0.1, 0.1), "y": (-0.25, 0.25), "z": (0.0, 0.0)}, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("object"), + }, + ) + + # Remove all the terms for the state machine demo + # TODO: Computing the root pose of deformable object from nodal positions is expensive. + # We need to fix that part before enabling these terms for the training. + self.terminations.object_dropping = None + self.rewards.reaching_object = None + self.rewards.lifting_object = None + self.rewards.object_goal_tracking = None + self.rewards.object_goal_tracking_fine_grained = None + self.observations.policy.object_position = None diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/ik_rel_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/ik_rel_env_cfg.py new file mode 100755 index 000000000000..1335fa8cf984 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/ik_rel_env_cfg.py @@ -0,0 +1,65 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass +from isaaclab.assets import RigidObjectCfg, ArticulationCfg +from . import joint_pos_env_cfg +import math +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +@configclass +class FrankaCubeEnvCfg(joint_pos_env_cfg.FrankaCubeEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + print("This is the base environment") + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace( + prim_path="{ENV_REGEX_NS}/Robot", + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + + "panda_joint1": 0.405, + "panda_joint2": 0.35, + "panda_joint3": -0.22, + "panda_joint4": -3.0, + "panda_joint5": -2.85, + "panda_joint6": math.pi / 2, # +90° → keeps hand level + "panda_joint7": 0.9, + "panda_finger_joint1": 0.04, # open gripper + "panda_finger_joint2": 0.04, + } + ), + ) + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + + +@configclass +class FrankaCubeEnvCfg_PLAY(FrankaCubeEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + print("This is the play environment") + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/joint_pos_env_cfg.py new file mode 100755 index 000000000000..f31e9d67339c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/joint_pos_env_cfg.py @@ -0,0 +1,102 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.assets import RigidObjectCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from isaaclab_tasks.manager_based.manipulation.cube_lift import mdp +from isaaclab_tasks.manager_based.manipulation.cube_lift.lift_env_cfg import CubeEnvCfg +from isaaclab_assets.glassware.glassware_objects import Chem_Assets +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip +from isaaclab_assets.robots.franka import FRANKA_PANDA_CFG # isort: skip + + +@configclass +class FrankaCubeEnvCfg(CubeEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + self.scene.robot = FRANKA_PANDA_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set actions for the specific robot type (franka) + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + scale=0.5, + use_default_offset=True + ) + self.actions.gripper_action = mdp.BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["panda_finger.*"], + open_command_expr={"panda_finger_.*": 0.04}, + close_command_expr={"panda_finger_.*": 0.0}, + ) + print("Action shape : ", self.actions.__dict__) + # Set the body name for the end effector + self.commands.object_pose.body_name = "panda_hand" + + # Set Cube as object + + #what if its now a vial rack ? + glassware = Chem_Assets() + # Set each stacking cube deterministically + self.scene.object = glassware.beaker(pos=[0.4, 0.0, 0.0203],name="Object") + + + + # Listens to the required transforms + marker_cfg = FRAME_MARKER_CFG.copy() + marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + marker_cfg.prim_path = "/Visuals/FrameTransformer" + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_link0", + debug_vis=False, + visualizer_cfg=marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_hand", + name="end_effector", + offset=OffsetCfg( + pos=[0.0, 0.0, 0.1034], + ), + ), + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_rightfinger", + name="tool_rightfinger", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.046), + ), + ), + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_leftfinger", + name="tool_leftfinger", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.046), + ), + ), + ], + ) + + +@configclass +class FrankaCubeEnvCfg_PLAY(FrankaCubeEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py new file mode 100755 index 000000000000..5fa05b6c8f4e --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py @@ -0,0 +1,362 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING +from enum import Enum +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg, DeformableObjectCfg, RigidObjectCfg, RigidObjectCollection, RigidObjectCollectionCfg +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import CurriculumTermCfg as CurrTerm +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import RewardTermCfg as RewTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import FrameTransformerCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +import warp as wp +from . import mdp +#from isaaclab.utils.logging_helper import LoggingHelper, ErrorType, LogType + + + + + +## +# Scene definition +## + + +@configclass +class ObjectTableSceneCfg(InteractiveSceneCfg): + """Configuration for the lift scene with a robot and a object. + This is the abstract base implementation, the exact scene is defined in the derived classes + which need to set the target object, robot and end-effector frames + """ + + # robots: will be populated by agent env cfg + robot: ArticulationCfg = MISSING + # end-effector sensor: will be populated by agent env cfg + ee_frame: FrameTransformerCfg = MISSING + # target object: will be populated by agent env cfg + object: RigidObjectCfg | DeformableObjectCfg = MISSING + + #for collision + # obstacle: RigidObjectCfg =MISSING + + + # glassware = RigidObjectCollectionCfg(rigid_objects={"vial" : vial, "flask" : flask}) + + + # Table + table = AssetBaseCfg( + prim_path="{ENV_REGEX_NS}/Table", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.5, 0, 0], rot=[0.707, 0, 0, 0.707]), + spawn=UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd"), + ) + + # plane + plane = AssetBaseCfg( + prim_path="/World/GroundPlane", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0, 0, -1.05]), + spawn=GroundPlaneCfg(), + ) + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + + + + +## +# MDP settings +## + + +@configclass +class CommandsCfg: + """Command terms for the MDP.""" +#### Goal Positions + object_pose = mdp.UniformPoseCommandCfg( + asset_name="robot", + body_name=MISSING, # will be set by agent env cfg + resampling_time_range=(10.0, 10.0), + debug_vis=True, + ranges=mdp.UniformPoseCommandCfg.Ranges( + # pos_x=(0.3,0.3), pos_y=(-0.28, -0.28), pos_z=(0.1, 0.1), roll=(0.0, 0.0), pitch=(0.0, 0.0), yaw=(0.0, 0.0) + pos_x=(0.4,0.4), pos_y=(-0.3, -0.3), pos_z=(0.08, 0.08), roll=(0.0, 0.0), pitch=(0.0, 0.0), yaw=(0.0, 0.0) + ), + ) + + midpoint_pose = mdp.UniformPoseCommandCfg( + asset_name="robot", + body_name="panda_hand", + resampling_time_range=(10.0, 10.0), + debug_vis=True, + ranges=mdp.UniformPoseCommandCfg.Ranges( + # pos_x=(0.3,0.3), pos_y=(-0.28, -0.28), pos_z=(0.1, 0.1), roll=(0.0, 0.0), pitch=(0.0, 0.0), yaw=(0.0, 0.0) + pos_x=(0,0), pos_y=(-0.25, -0.25), pos_z=(0.4, 0.4), roll=(0.0, 0.0), pitch=(0.0, 0.0), yaw=(0.0, 0.0) + ), + ) + + +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + # will be set by agent env cfg + arm_action: mdp.JointPositionActionCfg | mdp.DifferentialInverseKinematicsActionCfg = MISSING + gripper_action: mdp.BinaryJointPositionActionCfg = MISSING + + +@configclass +class ObservationsCfg(): + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + # def set_loghelper(self, loghelper: LoggingHelper): + # self.logstep.params["loghelper"] = loghelper + # logstep = ObsTerm(func = mdp.logstep) + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + object_position = ObsTerm(func=mdp.object_position_in_robot_root_frame) + target_object_position = ObsTerm(func=mdp.generated_commands, params={"command_name": "object_pose"}) + actions = ObsTerm(func=mdp.last_action) + # object_to_target = ObsTerm(func=mdp.object_near_goal) + eef_pos = ObsTerm(func=mdp.ee_frame_pos) + eef_quat = ObsTerm(func=mdp.ee_frame_quat) + gripper_pos = ObsTerm(func=mdp.gripper_pos) + #obstacle_pos = ObsTerm(func=mdp.obstacle_position_in_robot_root_frame) + #robot_pose=ObsTerm(func=mdp.robot_pose) + #object_to_target = ObsTerm(func=mdp.position_command_error) + #norm_error = ObsTerm(func=mdp.object_goal_norm_error) + abs_joint_pos = ObsTerm(func=mdp.get_joint_pos) + object_knocked = ObsTerm(func=mdp.object_knocked) + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class SubtaskCfg(ObsGroup): + """Observations for subtask group.""" + # def set_loghelper(self, loghelper: LoggingHelper): + # # Inject the logger into any terms that need it + # self.appr.params["loghelper"] = loghelper + # self.grasp.params["loghelper"] = loghelper + # self.lift.params["loghelper"] = loghelper + # self.appr_goal.params["loghelper"] = loghelper + #self.loghelper = loghelper + + + # appr = ObsTerm( + # func=mdp.reach_object, + # params={ + # "ee_frame_cfg": SceneEntityCfg("ee_frame"), + # "object_cfg": SceneEntityCfg("object"), + # "std" : 0.05 + # } + # ) + + grasp = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("object"), + }, + ) + # lift = ObsTerm( + # func=mdp.is_object_lifted, + # params={ + # "obj_cfg": SceneEntityCfg("object"), + # "threshold" : 0.1 + # } + # ) + appr_goal = ObsTerm( + func=mdp.object_near_goal, + params={ + "threshold": 0.05, + "command_name": "object_pose", + }, + ) + # release_object = ObsTerm( + # func=mdp.object_released, + # params={ + # "robot_cfg": SceneEntityCfg("robot"), + # "ee_frame_cfg": SceneEntityCfg("ee_frame"), + # "object_cfg": SceneEntityCfg("object"), + # }, + # ) + + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + + + + # observation groups + policy: PolicyCfg = PolicyCfg() + subtask_terms : SubtaskCfg = SubtaskCfg() + +@configclass +class EventCfg: + """Configuration for events.""" + ## add in log helper funcs + # def set_loghelper(self, loghelper: LoggingHelper): + # self.reset_all.params["loghelper"] = loghelper + reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset") + + reset_object_position = EventTerm( + func=mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": {"x": (0, 0.2), "y": (0, 0.25), "z": (0.05, 0.1)}, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("object", body_names="Object"), + }, + ) + #randomises the scale of the object + randomise_object_scale = EventTerm( + func=mdp.randomize_rigid_body_scale, + mode="prestartup", + # params={ + # "scale_range": {"x": (0.4, 0.7), "y": (0.4, 0.7), "z": (0.5, 0.7)}, + # "asset_cfg": SceneEntityCfg("object", body_names="Object"), + # }, + params={ + "scale_range": {"x": (0.5, 0.5), "y": (0.5, 0.5), "z": (0.6, 0.7)}, + "asset_cfg": SceneEntityCfg("object", body_names="Object"), + }, + ) + + + +@configclass +class RewardsCfg: + """Reward terms for the MDP.""" + + reaching_object = RewTerm(func=mdp.object_ee_distance, params={"std": 0.1}, weight=1.0) + + lifting_object = RewTerm(func=mdp.object_is_lifted, params={"minimal_height": 0.14}, weight=30.0) + + reaching_target = RewTerm(func=mdp.object_goal_norm_error, weight = 20.0) + + object_goal_tracking = RewTerm( + func=mdp.object_goal_distance, + params={"std": 0.3, "minimal_height": 0.04, "command_name": "object_pose"}, + weight=16.0, + ) + + object_goal_tracking_fine_grained = RewTerm( + func=mdp.object_goal_distance, + params={"std": 0.05, "minimal_height": 0.04, "command_name": "object_pose"}, + weight=5.0, + ) + + # action penalty + action_rate = RewTerm(func=mdp.action_rate_l2, weight=-1e-4) + + joint_vel = RewTerm( + func=mdp.joint_vel_l2, + weight=-1e-4, + params={"asset_cfg": SceneEntityCfg("robot")}, + ) + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + # def set_loghelper(self, loghelper: LoggingHelper): + # self.success.params["loghelper"] = loghelper + # self.time_out.params["loghelper"] = loghelper + # self.object_dropping.params["loghelper"] = loghelper + # # self.loghelper = loghelper + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + #joint_violation = DoneTerm(func=mdp.joint_pos_out_of_limit) + + #object_orientation = DoneTerm(func=mdp.bad_orientation) + + #joint_effort = DoneTerm(func=mdp.joint_effort_out_of_limit) + + object_dropping = DoneTerm( + func=mdp.root_height_below_minimum, params={"minimum_height": -0.05, "asset_cfg": SceneEntityCfg("object")} + ) + #fix object dropping + success = DoneTerm(func=mdp.object_near_goal) + + +@configclass +class CurriculumCfg: + """Curriculum terms for the MDP.""" + + action_rate = CurrTerm( + func=mdp.modify_reward_weight, params={"term_name": "action_rate", "weight": -1e-1, "num_steps": 10000} + ) + + joint_vel = CurrTerm( + func=mdp.modify_reward_weight, params={"term_name": "joint_vel", "weight": -1e-1, "num_steps": 10000} + ) + + +## +# Environment configuration +## + + + +@configclass +class CubeEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the lifting environment.""" + # loghelper = LoggingHelper() + # Scene settings + scene: ObjectTableSceneCfg = ObjectTableSceneCfg(num_envs=4096, env_spacing=2.5, replicate_physics=False) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + commands: CommandsCfg = CommandsCfg() + # MDP settings + rewards: RewardsCfg = RewardsCfg() + terminations: TerminationsCfg = TerminationsCfg() + events: EventCfg = EventCfg() + curriculum: CurriculumCfg = CurriculumCfg() + + #subtask_statemachine: SubtaskStateMachine = SubtaskStateMachine() + + def __post_init__(self): + """Post initialization.""" + print("debug cfg : ", self.observations.policy.actions) + # general settings + + self.decimation = 2 + self.episode_length_s = 60 + # simulation settings + self.sim.dt = 0.01 # 100Hz + self.sim.render_interval = self.decimation + self.viewer.eye = (1.0, 1.0, 1.0) + self.viewer.lookat = (0.0, 0.0, -0.1) + # self.terminations.set_loghelper(self.loghelper) + #self.events.set_loghelper(self.loghelper) + # self.observations.policy.set_loghelper(self.loghelper) + # self.observations.subtask_terms.set_loghelper(self.loghelper) + self.sim.physx.bounce_threshold_velocity = 0.2 + self.sim.physx.bounce_threshold_velocity = 0.01 + # self.sim.physx.gpu_found_lost_aggregate_pairs_capacity = 1024 * 1024 * 4 + # self.sim.physx.gpu_total_aggregate_pairs_capacity = 16 * 1024 + self.sim.physx.friction_correlation_distance = 0.00625 \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/__init__.py new file mode 100755 index 000000000000..f53d3692aa6d --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This sub-module contains the functions that are specific to the lift environments.""" + +from isaaclab.envs.mdp import * # noqa: F401, F403 + +from .observations import * # noqa: F401, F403 +from .rewards import * # noqa: F401, F403 +from .terminations import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py new file mode 100755 index 000000000000..162823fa1b64 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py @@ -0,0 +1,309 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import torch +from typing import TYPE_CHECKING + +from isaaclab.assets import RigidObject +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils.math import subtract_frame_transforms, combine_frame_transforms +from isaaclab.assets import Articulation, RigidObject, RigidObjectCollection +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils.math import matrix_from_quat +from isaaclab.sensors import FrameTransformer +#from isaaclab.utils.logging_helper import LoggingHelper, ErrorType, LogType + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def object_position_in_robot_root_frame( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + # loghelper : LoggingHelper = LoggingHelper() +) -> torch.Tensor: + """The position of the object in the robot's root frame.""" + robot: RigidObject = env.scene[robot_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + object_pos_w = object.data.root_pos_w[:, :3] + object_pos_b, _ = subtract_frame_transforms( + robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], object_pos_w + ) + + return object_pos_b + +def obstacle_position_in_robot_root_frame( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_cfg: SceneEntityCfg = SceneEntityCfg("obstacle"), + # loghelper : LoggingHelper = LoggingHelper() +) -> torch.Tensor: + """The position of the object in the robot's root frame.""" + robot: RigidObject = env.scene[robot_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + object_pos_w = object.data.root_pos_w[:, :3] + object_pos_b, _ = subtract_frame_transforms( + robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], object_pos_w + ) + + return object_pos_b + + +def reach_object( + env: ManagerBasedRLEnv, + std: float = 0.05, + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), + # loghelper : LoggingHelper = LoggingHelper() +) -> torch.Tensor: + """Reward the agent for reaching the object using tanh-kernel.""" + # extract the used quantities (to enable type-hinting) + object: RigidObject = env.scene[object_cfg.name] + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + # Target object position: (num_envs, 3) + cube_pos_w = object.data.root_pos_w + # End-effector position: (num_envs, 3) + ee_w = ee_frame.data.target_pos_w[..., 0, :] + # print("ee pos : ", ee_w) + # Distance of the end-effector to the object: (num_envs,) + object_ee_distance = torch.norm(cube_pos_w - ee_w, dim=1) + #if object_ee_distance.item() < std : + # print(f"Observed Object Reached : {object_ee_distance.item()}") + # loghelper.logsubtask(LogType.APPR) + # if object_ee_distance[0] < std: + # print(f"Reached object, dist :{object_ee_distance.item()}") + return object_ee_distance < std + +def object_grasped( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg= SceneEntityCfg("robot"), + ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), + object_cfg: SceneEntityCfg= SceneEntityCfg("object"), + diff_threshold: float = 0.06, + gripper_open_val: torch.tensor = torch.tensor([0.04]), + gripper_threshold: float = 0.005, + # loghelper : LoggingHelper = LoggingHelper() +) -> torch.Tensor: + """Check if an object is grasped by the specified robot.""" + + robot: Articulation = env.scene[robot_cfg.name] + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + + object_pos = object.data.root_pos_w + end_effector_pos = ee_frame.data.target_pos_w[:, 0, :] + pose_diff = torch.linalg.vector_norm(object_pos - end_effector_pos, dim=1) + + grasped = torch.logical_and( + pose_diff < diff_threshold, + torch.abs(robot.data.joint_pos[:, -1] - gripper_open_val.to(env.device)) > gripper_threshold, + ) + grasped = torch.logical_and( + grasped, torch.abs(robot.data.joint_pos[:, -2] - gripper_open_val.to(env.device)) > gripper_threshold + ) + #if grasped[0]: + # print(f"Observed Object grasped : {grasped.item()}") + # loghelper.logsubtask(LogType.GRASP) + return grasped + +def object_released( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg, + ee_frame_cfg: SceneEntityCfg, + object_cfg: SceneEntityCfg, + diff_threshold: float = 0.06, + gripper_open_val: torch.tensor = torch.tensor([0.04]), + gripper_threshold: float = 0.035, + # loghelper : LoggingHelper = LoggingHelper() +) -> torch.Tensor: + """Check if an object is grasped by the specified robot.""" + + robot: Articulation = env.scene[robot_cfg.name] + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + + object_pos = object.data.root_pos_w + end_effector_pos = ee_frame.data.target_pos_w[:, 0, :] + pose_diff = torch.linalg.vector_norm(object_pos - end_effector_pos, dim=1) + + grasped = torch.logical_and( + pose_diff < diff_threshold, + torch.abs(robot.data.joint_pos[:, -1]) > gripper_threshold, + ) + grasped = torch.logical_and( + grasped, torch.abs(robot.data.joint_pos[:, -2] ) > gripper_threshold + ) + # if grasped[0]: + # print(f"Observed Object grasped : {grasped.item()}") + # loghelper.logsubtask(LogType.GRASP) + return grasped + +def is_object_lifted( + env: ManagerBasedRLEnv, + obj_cfg: SceneEntityCfg = SceneEntityCfg("object"), + threshold : float = 0.05, + #loghelper : LoggingHelper = LoggingHelper() +): + #return true when object z coord above a threshold value + object = env.scene[obj_cfg.name] + # if object.data.root_pos_w[:, 2].item() > threshold : + # print(f"Observed Object Lifted : {object.data.root_pos_w[:, 2].item()}") + # loghelper.logsubtask(LogType.LIFT) + + return object.data.root_pos_w[:, 2] > threshold + + +def ee_frame_pos(env: ManagerBasedRLEnv, ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame")) -> torch.Tensor: + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + ee_frame_pos = ee_frame.data.target_pos_w[:, 0, :] - env.scene.env_origins[:, 0:3] + + return ee_frame_pos + + +def ee_frame_quat(env: ManagerBasedRLEnv, ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame")) -> torch.Tensor: + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + ee_frame_quat = ee_frame.data.target_quat_w[:, 0, :] + + return ee_frame_quat + + +def gripper_pos(env: ManagerBasedRLEnv, robot_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + robot: Articulation = env.scene[robot_cfg.name] + finger_joint_1 = robot.data.joint_pos[:, -1].clone().unsqueeze(1) + finger_joint_2 = -1 * robot.data.joint_pos[:, -2].clone().unsqueeze(1) + + return torch.cat((finger_joint_1, finger_joint_2), dim=1) + +def object_near_goal( + env: ManagerBasedRLEnv, + command_name: str = "object_pose", + threshold: float = 0.05, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + # loghelper : LoggingHelper = LoggingHelper() +) -> torch.Tensor: + """Termination condition for the object reaching the goal position. + + Args: + env: The environment. + command_name: The name of the command that is used to control the object. + threshold: The threshold for the object to reach the goal position. Defaults to 0.02. + robot_cfg: The robot configuration. Defaults to SceneEntityCfg("robot"). + object_cfg: The object configuration. Defaults to SceneEntityCfg("object"). + + """ + robot: RigidObject = env.scene[robot_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + command = env.command_manager.get_command(command_name) + object_pos_w = object.data.root_pos_w[:, :3] + object_pos_b, _ = subtract_frame_transforms( + robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], object_pos_w + ) + des_pos_b = command[:, :3] + # print(f"desired position : {des_pos_b}, actual position : {object_pos_b}") + error = torch.norm(des_pos_b - object_pos_b, dim=1) + #if error torch.Tensor: + """Termination condition for the object reaching the goal position. + + Args: + env: The environment. + command_name: The name of the command that is used to control the object. + threshold: The threshold for the object to reach the goal position. Defaults to 0.02. + robot_cfg: The robot configuration. Defaults to SceneEntityCfg("robot"). + object_cfg: The object configuration. Defaults to SceneEntityCfg("object"). + + """ + robot: RigidObject = env.scene[robot_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + command = env.command_manager.get_command(command_name) + object_pos_w = object.data.root_pos_w[:, :3] + object_pos_b, _ = subtract_frame_transforms( + robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], object_pos_w + ) + des_pos_b = command[:, :3] + # print(f"desired position : {des_pos_b}, actual position : {object_pos_b}") + error = torch.norm(des_pos_b - object_pos_b, dim=1) + #print("norm position error : ", error) + return error + +def robot_pose( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot") +): + robot: Articulation = env.scene[robot_cfg.name] + # print(robot.data.joint_pos) + return torch.tensor([0.04]) + +def position_command_error( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + command_name: str = "object_pose", + # loghelper : LoggingHelper = LoggingHelper() +) -> torch.Tensor: + """The position of the object in the robot's root frame.""" + robot: RigidObject = env.scene[robot_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + command = env.command_manager.get_command(command_name) + object_pos_w = object.data.root_pos_w[:, :3] + object_pos_b, _ = subtract_frame_transforms( + robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], object_pos_w + ) + des_pos_b = command[:, :3] + # print(f"desired position : {des_pos_b}, actual position : {object_pos_b}") + error = torch.sub(des_pos_b, object_pos_b) + #print("position error : ", error) + return error + +def get_joint_pos(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: + """The joint positions of the asset. + + Note: Only the joints configured in :attr:`asset_cfg.joint_ids` will have their positions returned. + """ + # extract the used quantities (to enable type-hinting) + asset: Articulation = env.scene[asset_cfg.name] + #print("Joint positions: : ", asset.data.joint_pos[:, asset_cfg.joint_ids]) + return asset.data.joint_pos[:, asset_cfg.joint_ids] + +# def logstep( +# env: ManagerBasedRLEnv, +# # loghelper : LoggingHelper +# ): +# loghelper.logstep() +# return torch.tensor([0]) +def upright_tilt_deg(q_cur, q_init) -> torch.Tensor: + u_cur, u_init = matrix_from_quat(q_cur)[:, :, 2], matrix_from_quat(q_init)[:, :, 2] + return torch.rad2deg(torch.acos((u_cur * u_init).sum(-1).clamp(-1.0, 1.0))) + + + +def object_tilt (env: ManagerBasedRLEnv, upper_object_cfg: SceneEntityCfg = SceneEntityCfg("object")): + object: RigidObject = env.scene[upper_object_cfg.name] + tilt_deg = upright_tilt_deg(object.data.root_quat_w, object.data.default_root_state[:, 3:7]) + #print("obs term : tilt : ", tilt_deg) + return tilt_deg + +def object_knocked(env: ManagerBasedRLEnv, upper_object_cfg: SceneEntityCfg = SceneEntityCfg("object"), max_tilt: float = 90): + object: RigidObject = env.scene[upper_object_cfg.name] + tilt_deg = upright_tilt_deg(object.data.root_quat_w, object.data.default_root_state[:, 3:7]) + if tilt_deg.cpu().detach().numpy()[0] > max_tilt: + print("object knocked") + return torch.tensor([True], device='cuda:0') + else :return torch.tensor([False], device='cuda:0') \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/rewards.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/rewards.py new file mode 100755 index 000000000000..4436f10c7e48 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/rewards.py @@ -0,0 +1,73 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import torch +from typing import TYPE_CHECKING + +from isaaclab.assets import RigidObject +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import FrameTransformer +from isaaclab.utils.math import combine_frame_transforms +#from isaaclab.utils.logging_helper import LoggingHelper, ErrorType, LogType + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def object_is_lifted( + env: ManagerBasedRLEnv, minimal_height: float, object_cfg: SceneEntityCfg = SceneEntityCfg("object") +) -> torch.Tensor: + """Reward the agent for lifting the object above the minimal height.""" + object: RigidObject = env.scene[object_cfg.name] + + return torch.where(object.data.root_pos_w[:, 2] > minimal_height, 1.0, 0.0) + + +def object_ee_distance( + env: ManagerBasedRLEnv, + std: float, + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), +) -> torch.Tensor: + """Reward the agent for reaching the object using tanh-kernel.""" + # extract the used quantities (to enable type-hinting) + object: RigidObject = env.scene[object_cfg.name] + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + # Target object position: (num_envs, 3) + cube_pos_w = object.data.root_pos_w + # End-effector position: (num_envs, 3) + ee_w = ee_frame.data.target_pos_w[..., 0, :] + # Distance of the end-effector to the object: (num_envs,) + object_ee_distance = torch.norm(cube_pos_w - ee_w, dim=1) + + return 1 - torch.tanh(object_ee_distance / std) + + + +def object_goal_distance( + env: ManagerBasedRLEnv, + std: float, + minimal_height: float, + command_name: str, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), +) -> torch.Tensor: + """Reward the agent for tracking the goal pose using tanh-kernel.""" + # extract the used quantities (to enable type-hinting) + robot: RigidObject = env.scene[robot_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + command = env.command_manager.get_command(command_name) + # compute the desired position in the world frame + des_pos_b = command[:, :3] + des_pos_w, _ = combine_frame_transforms(robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], des_pos_b) + # distance of the end-effector to the object: (num_envs,) + distance = torch.norm(des_pos_w - object.data.root_pos_w[:, :3], dim=1) + # rewarded if the object is lifted above the threshold + #if (object.data.root_pos_w[:, 2] > minimal_height) * (1 - torch.tanh(distance / std)): + # print(f"object goal distance : {distance} ") + #print(f" Goal position reward : {(object.data.root_pos_w[:, 2] > minimal_height) * (1 - torch.tanh(distance / std))}") + return (object.data.root_pos_w[:, 2] > minimal_height) * (1 - torch.tanh(distance / std)) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/terminations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/terminations.py new file mode 100755 index 000000000000..007a82d6ca83 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/terminations.py @@ -0,0 +1,97 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Common functions that can be used to activate certain terminations for the lift task. + +The functions can be passed to the :class:`isaaclab.managers.TerminationTermCfg` object to enable +the termination introduced by the function. +""" + +from __future__ import annotations + +import torch +from typing import TYPE_CHECKING + +from isaaclab.assets import RigidObject, Articulation +from isaaclab.managers import SceneEntityCfg + +from isaaclab.sensors import FrameTransformer +from isaaclab.utils.math import combine_frame_transforms, subtract_frame_transforms +#from isaaclab.utils.logging_helper import LoggingHelper, ErrorType, LogType +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def object_reached_goal( + env: ManagerBasedRLEnv, + command_name: str = "object_pose", + threshold: float = 0.02, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + # loghelper : LoggingHelper = LoggingHelper() +) -> torch.Tensor: + """Termination condition for the object reaching the goal position. + + Args: + env: The environment. + command_name: The name of the command that is used to control the object. + threshold: The threshold for the object to reach the goal position. Defaults to 0.02. + robot_cfg: The robot configuration. Defaults to SceneEntityCfg("robot"). + object_cfg: The object configuration. Defaults to SceneEntityCfg("object"). + + """ + # extract the used quantities (to enable type-hinting) + robot: RigidObject = env.scene[robot_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + command = env.command_manager.get_command(command_name) + # compute the desired position in the world frame + des_pos_b = command[:, :3] + des_pos_w, _ = combine_frame_transforms(robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], des_pos_b) + # distance of the end-effector to the object: (num_envs,) + distance = torch.norm(des_pos_w - object.data.root_pos_w[:, :3], dim=1) + # print(f"For DEBUG : DISTANCE TO GOAL : {distance}") + # if(distance.item() < threshold): + # loghelper.logsubtask(LogType.FINISH) + test = distance < threshold + if (torch.any(test)): + print("debug : terminations : ", torch.any(test)) + return distance < threshold + + +def placed( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + + object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + ee_frame_cfg: SceneEntityCfg=SceneEntityCfg("ee_frame"), + command_name: str = "object_pose", + threshold: torch.tensor = torch.tensor([0.05], device="cuda:0"), + gripper_open_val= torch.tensor([0.038], device="cuda:0"), #open + atol=0.0001, + rtol=0.0001, + gripper_threshold: float = 0.005, +): + robot: Articulation = env.scene[robot_cfg.name] + + command = env.command_manager.get_command(command_name) + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name], + object: RigidObject = env.scene[object_cfg.name] + + des_pos_b = command[:, :3] + des_pos_w, _ = combine_frame_transforms(robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], des_pos_b) + # distance of the end-effector to the object: (num_envs,) + + object_pos = object.data.root_pos_w + #end_effector_pos = ee_frame.data.target_pos_w[:, 0, :] + # pose_diff = torch.linalg.vector_norm(object_pos - end_effector_pos, dim=1) + distance = torch.norm(des_pos_w - object_pos[:, :3], dim=1) + object_position = distance gripper_open_val) + gripper_open = robot.data.joint_pos[:, -1] > gripper_open_val + state = torch.logical_and(object_position, gripper_open) + return state \ No newline at end of file From 148f1e7a2155b827c20b6a8aa9d6a1b051a0b91e Mon Sep 17 00:00:00 2001 From: LJones-alt Date: Wed, 15 Oct 2025 17:35:52 +0100 Subject: [PATCH 02/15] some additional updates --- docs/conical_flask.obj | 3 + docs/ika_plate.obj | 3 + docs/sample_vial.obj | 3 + docs/scripts/bc.py | 985 +++++++++++ docs/scripts/policy_nets.py | 1570 +++++++++++++++++ .../bc_new_demos/20250925163039}/config.json | 4 +- .../bc_new_demos/20250925163039}/config.json | 4 +- .../bc_new_demos/20250925163039}/config.json | 18 +- .../bc_new_demos/20250925163039}/config.json | 18 +- .../bc_new_demos/20250925163039/config.json | 216 +++ .../bc_high_data_/20251009101852/config.json | 219 +++ .../bc_high_data_/20251009101852/config.json | 219 +++ .../bc_high_data_/20251009101852/config.json | 219 +++ .../bc_high_data_/20251009101852/config.json | 219 +++ .../bc_high_data_/20251009101852/config.json | 219 +++ .../bc_high_data_/20251009101852/config.json | 219 +++ .../bc_high_data_/20251009101852/config.json | 219 +++ .../bc_high_data_/20251009101852/config.json | 219 +++ .../bc_high_data_/20251009101852/config.json | 219 +++ .../bc_high_data_/20251009101852/config.json | 219 +++ .../bcq/20250721123120/config.json | 315 ---- .../bc_med_data_/20251015094236/config.json | 219 +++ .../bc_med_data_/20251015094238/config.json | 219 +++ .../bc_med_data_/20251015094238/config.json | 219 +++ .../bc_med_data_/20251015094239/config.json | 219 +++ .../bc_med_data_/20251015094239/config.json | 219 +++ .../bc_trans/20250715154020/config.json | 216 --- .../bc_trans/20250721074506/config.json | 216 --- .../bcq/20250721135829/config.json | 315 ---- .../franks_stack/20250715152538/config.json | 210 --- .../franks_stack/20250716100129/config.json | 210 --- .../20251008103600/config.json | 219 +++ .../20251008103703/config.json | 219 +++ .../20251008113716/config.json | 219 +++ .../20251008113737/config.json | 219 +++ .../20251008124639/config.json | 219 +++ .../20251008131706/config.json | 219 +++ .../bc_low_demos/20251007171556/config.json | 219 +++ .../robomimic/play_ensemble.py | 37 +- .../robomimic/train_ensemble.py | 25 +- scripts/tools/copy_metadata_hdf5.py | 32 + scripts/tools/hdf5_splitter.py | 60 + source/isaaclab/isaaclab/envs/mdp/events.py | 152 +- .../datasets/hdf5_dataset_file_handler.py | 10 +- .../isaaclab_assets/__init__.py | 1 + .../envs/cube_blueprint_mimic_env_cfg.py | 2 +- .../isaaclab_mimic/envs/cube_mimic_env_cfg.py | 2 +- .../cube_lift/config/franka/__init__.py | 5 +- .../agents/robomimic/bc_rnn_low_dim.json | 31 +- .../robomimic/{bc.json => bc_temp.json} | 0 .../cube_lift/config/franka/dev_env_cfg.py | 42 +- .../config/franka/dev_ik_rel_env_obs.py | 48 +- .../manipulation/cube_lift/lift_env_cfg.py | 11 +- 53 files changed, 7899 insertions(+), 1683 deletions(-) create mode 100644 docs/conical_flask.obj create mode 100644 docs/ika_plate.obj create mode 100644 docs/sample_vial.obj create mode 100644 docs/scripts/bc.py create mode 100644 docs/scripts/policy_nets.py rename docs/training_runs/{bc_rnn_ensemble_pick_place_new_start/Dev-IK-Rel-v0/model0/bc_new_demos/20250924112632 => Dev-IK-Rel-v0/model0/bc_new_demos/20250925163039}/config.json (97%) rename docs/training_runs/{bc_rnn_ensemble_pick_place_new_start/Dev-IK-Rel-v0/model0/bc_new_demos/20250924123612 => Dev-IK-Rel-v0/model1/bc_new_demos/20250925163039}/config.json (97%) rename docs/training_runs/{bc_trans/bc_trans/20250721085238 => Dev-IK-Rel-v0/model2/bc_new_demos/20250925163039}/config.json (94%) mode change 100755 => 100644 rename docs/training_runs/{bc_trans/bc_trans/20250721074750 => Dev-IK-Rel-v0/model3/bc_new_demos/20250925163039}/config.json (94%) mode change 100755 => 100644 create mode 100644 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source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/{bc.json => bc_temp.json} (100%) diff --git a/docs/conical_flask.obj b/docs/conical_flask.obj new file mode 100644 index 000000000000..1cf78fb5cfe5 --- /dev/null +++ b/docs/conical_flask.obj @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:42056c414f1fec1b69d48dcbf4e24e6b79200ab60e99775425505f320ce067c5 +size 868444 diff --git a/docs/ika_plate.obj b/docs/ika_plate.obj new file mode 100644 index 000000000000..1a0738e552fc --- /dev/null +++ b/docs/ika_plate.obj @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c0de6bafe0e4b876dfd3a9e5fad29f14c8b35af860386ef81c7e9e6312d44e7d +size 46251 diff --git a/docs/sample_vial.obj b/docs/sample_vial.obj new file mode 100644 index 000000000000..bb777506e473 --- /dev/null +++ b/docs/sample_vial.obj @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ecbe68889e998563caf5dfbf5be3e8e98bdbc13920c2d23313be92dde78426eb +size 3029229 diff --git a/docs/scripts/bc.py b/docs/scripts/bc.py new file mode 100644 index 000000000000..a31b5e816a70 --- /dev/null +++ b/docs/scripts/bc.py @@ -0,0 +1,985 @@ +""" +Implementation of Behavioral Cloning (BC). +""" +from collections import OrderedDict + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.distributions as D + +import robomimic.models.base_nets as BaseNets +import robomimic.models.obs_nets as ObsNets +import robomimic.models.policy_nets as PolicyNets +import robomimic.models.vae_nets as VAENets +import robomimic.utils.loss_utils as LossUtils +import robomimic.utils.tensor_utils as TensorUtils +import robomimic.utils.torch_utils as TorchUtils +import robomimic.utils.obs_utils as ObsUtils + +from robomimic.algo import register_algo_factory_func, PolicyAlgo + + +@register_algo_factory_func("bc") +def algo_config_to_class(algo_config): + """ + Maps algo config to the BC algo class to instantiate, along with additional algo kwargs. + + Args: + algo_config (Config instance): algo config + + Returns: + algo_class: subclass of Algo + algo_kwargs (dict): dictionary of additional kwargs to pass to algorithm + """ + + # note: we need the check below because some configs import BCConfig and exclude + # some of these options + gaussian_enabled = ("gaussian" in algo_config and algo_config.gaussian.enabled) + gmm_enabled = ("gmm" in algo_config and algo_config.gmm.enabled) + vae_enabled = ("vae" in algo_config and algo_config.vae.enabled) + + rnn_enabled = algo_config.rnn.enabled + # support legacy configs that do not have "transformer" item + transformer_enabled = ("transformer" in algo_config) and algo_config.transformer.enabled + + if gaussian_enabled: + if rnn_enabled: + algo_class, algo_kwargs = BC_RNN_GAUSSIAN, {} + elif transformer_enabled: + raise NotImplementedError + else: + algo_class, algo_kwargs = BC_Gaussian, {} + elif gmm_enabled: + if rnn_enabled: + algo_class, algo_kwargs = BC_RNN_GMM, {} + elif transformer_enabled: + algo_class, algo_kwargs = BC_Transformer_GMM, {} + else: + algo_class, algo_kwargs = BC_GMM, {} + elif vae_enabled: + if rnn_enabled: + raise NotImplementedError + elif transformer_enabled: + raise NotImplementedError + else: + algo_class, algo_kwargs = BC_VAE, {} + else: + if rnn_enabled: + algo_class, algo_kwargs = BC_RNN, {} + elif transformer_enabled: + algo_class, algo_kwargs = BC_Transformer, {} + else: + algo_class, algo_kwargs = BC, {} + + return algo_class, algo_kwargs + + +class BC(PolicyAlgo): + """ + Normal BC training. + """ + def _create_networks(self): + """ + Creates networks and places them into @self.nets. + """ + self.nets = nn.ModuleDict() + self.nets["policy"] = PolicyNets.ActorNetwork( + obs_shapes=self.obs_shapes, + goal_shapes=self.goal_shapes, + ac_dim=self.ac_dim, + mlp_layer_dims=self.algo_config.actor_layer_dims, + encoder_kwargs=ObsUtils.obs_encoder_kwargs_from_config(self.obs_config.encoder), + ) + self.nets = self.nets.float().to(self.device) + + def process_batch_for_training(self, batch): + """ + Processes input batch from a data loader to filter out + relevant information and prepare the batch for training. + + Args: + batch (dict): dictionary with torch.Tensors sampled + from a data loader + + Returns: + input_batch (dict): processed and filtered batch that + will be used for training + """ + input_batch = dict() + input_batch["obs"] = {k: batch["obs"][k][:, 0, :] for k in batch["obs"]} + input_batch["goal_obs"] = batch.get("goal_obs", None) # goals may not be present + input_batch["actions"] = batch["actions"][:, 0, :] + # we move to device first before float conversion because image observation modalities will be uint8 - + # this minimizes the amount of data transferred to GPU + return TensorUtils.to_float(TensorUtils.to_device(input_batch, self.device)) + + + def train_on_batch(self, batch, epoch, validate=False): + """ + Training on a single batch of data. + + Args: + batch (dict): dictionary with torch.Tensors sampled + from a data loader and filtered by @process_batch_for_training + + epoch (int): epoch number - required by some Algos that need + to perform staged training and early stopping + + validate (bool): if True, don't perform any learning updates. + + Returns: + info (dict): dictionary of relevant inputs, outputs, and losses + that might be relevant for logging + """ + with TorchUtils.maybe_no_grad(no_grad=validate): + info = super(BC, self).train_on_batch(batch, epoch, validate=validate) + predictions = self._forward_training(batch) + losses = self._compute_losses(predictions, batch) + + info["predictions"] = TensorUtils.detach(predictions) + info["losses"] = TensorUtils.detach(losses) + + if not validate: + step_info = self._train_step(losses) + info.update(step_info) + + return info + + def _forward_training(self, batch): + """ + Internal helper function for BC algo class. Compute forward pass + and return network outputs in @predictions dict. + + Args: + batch (dict): dictionary with torch.Tensors sampled + from a data loader and filtered by @process_batch_for_training + + Returns: + predictions (dict): dictionary containing network outputs + """ + predictions = OrderedDict() + actions = self.nets["policy"](obs_dict=batch["obs"], goal_dict=batch["goal_obs"]) + predictions["actions"] = actions + return predictions + + def _compute_losses(self, predictions, batch): + """ + Internal helper function for BC algo class. Compute losses based on + network outputs in @predictions dict, using reference labels in @batch. + + Args: + predictions (dict): dictionary containing network outputs, from @_forward_training + batch (dict): dictionary with torch.Tensors sampled + from a data loader and filtered by @process_batch_for_training + + Returns: + losses (dict): dictionary of losses computed over the batch + """ + losses = OrderedDict() + a_target = batch["actions"] + actions = predictions["actions"] + losses["l2_loss"] = nn.MSELoss()(actions, a_target) + losses["l1_loss"] = nn.SmoothL1Loss()(actions, a_target) + # cosine direction loss on eef delta position + losses["cos_loss"] = LossUtils.cosine_loss(actions[..., :3], a_target[..., :3]) + + action_losses = [ + self.algo_config.loss.l2_weight * losses["l2_loss"], + self.algo_config.loss.l1_weight * losses["l1_loss"], + self.algo_config.loss.cos_weight * losses["cos_loss"], + ] + action_loss = sum(action_losses) + losses["action_loss"] = action_loss + return losses + + def _train_step(self, losses): + """ + Internal helper function for BC algo class. Perform backpropagation on the + loss tensors in @losses to update networks. + + Args: + losses (dict): dictionary of losses computed over the batch, from @_compute_losses + """ + + # gradient step + info = OrderedDict() + policy_grad_norms = TorchUtils.backprop_for_loss( + net=self.nets["policy"], + optim=self.optimizers["policy"], + loss=losses["action_loss"], + ) + info["policy_grad_norms"] = policy_grad_norms + return info + + def log_info(self, info): + """ + Process info dictionary from @train_on_batch to summarize + information to pass to tensorboard for logging. + + Args: + info (dict): dictionary of info + + Returns: + loss_log (dict): name -> summary statistic + """ + log = super(BC, self).log_info(info) + log["Loss"] = info["losses"]["action_loss"].item() + if "l2_loss" in info["losses"]: + log["L2_Loss"] = info["losses"]["l2_loss"].item() + if "l1_loss" in info["losses"]: + log["L1_Loss"] = info["losses"]["l1_loss"].item() + if "cos_loss" in info["losses"]: + log["Cosine_Loss"] = info["losses"]["cos_loss"].item() + if "policy_grad_norms" in info: + log["Policy_Grad_Norms"] = info["policy_grad_norms"] + return log + + def get_action(self, obs_dict, goal_dict=None): + """ + Get policy action outputs. + + Args: + obs_dict (dict): current observation + goal_dict (dict): (optional) goal + + Returns: + action (torch.Tensor): action tensor + """ + assert not self.nets.training + return self.nets["policy"](obs_dict, goal_dict=goal_dict) + + +class BC_Gaussian(BC): + """ + BC training with a Gaussian policy. + """ + def _create_networks(self): + """ + Creates networks and places them into @self.nets. + """ + assert self.algo_config.gaussian.enabled + + self.nets = nn.ModuleDict() + self.nets["policy"] = PolicyNets.GaussianActorNetwork( + obs_shapes=self.obs_shapes, + goal_shapes=self.goal_shapes, + ac_dim=self.ac_dim, + mlp_layer_dims=self.algo_config.actor_layer_dims, + fixed_std=self.algo_config.gaussian.fixed_std, + init_std=self.algo_config.gaussian.init_std, + std_limits=(self.algo_config.gaussian.min_std, 7.5), + std_activation=self.algo_config.gaussian.std_activation, + low_noise_eval=self.algo_config.gaussian.low_noise_eval, + encoder_kwargs=ObsUtils.obs_encoder_kwargs_from_config(self.obs_config.encoder), + ) + + self.nets = self.nets.float().to(self.device) + + def _forward_training(self, batch): + """ + Internal helper function for BC algo class. Compute forward pass + and return network outputs in @predictions dict. + + Args: + batch (dict): dictionary with torch.Tensors sampled + from a data loader and filtered by @process_batch_for_training + + Returns: + predictions (dict): dictionary containing network outputs + """ + dists = self.nets["policy"].forward_train( + obs_dict=batch["obs"], + goal_dict=batch["goal_obs"], + ) + + # make sure that this is a batch of multivariate action distributions, so that + # the log probability computation will be correct + assert len(dists.batch_shape) == 1 + log_probs = dists.log_prob(batch["actions"]) + + predictions = OrderedDict( + log_probs=log_probs, + ) + return predictions + + def _compute_losses(self, predictions, batch): + """ + Internal helper function for BC algo class. Compute losses based on + network outputs in @predictions dict, using reference labels in @batch. + + Args: + predictions (dict): dictionary containing network outputs, from @_forward_training + batch (dict): dictionary with torch.Tensors sampled + from a data loader and filtered by @process_batch_for_training + + Returns: + losses (dict): dictionary of losses computed over the batch + """ + + # loss is just negative log-likelihood of action targets + action_loss = -predictions["log_probs"].mean() + return OrderedDict( + log_probs=-action_loss, + action_loss=action_loss, + ) + + def log_info(self, info): + """ + Process info dictionary from @train_on_batch to summarize + information to pass to tensorboard for logging. + + Args: + info (dict): dictionary of info + + Returns: + loss_log (dict): name -> summary statistic + """ + log = PolicyAlgo.log_info(self, info) + log["Loss"] = info["losses"]["action_loss"].item() + log["Log_Likelihood"] = info["losses"]["log_probs"].item() + if "policy_grad_norms" in info: + log["Policy_Grad_Norms"] = info["policy_grad_norms"] + return log + + +class BC_GMM(BC_Gaussian): + """ + BC training with a Gaussian Mixture Model policy. + """ + def _create_networks(self): + """ + Creates networks and places them into @self.nets. + """ + assert self.algo_config.gmm.enabled + + self.nets = nn.ModuleDict() + self.nets["policy"] = PolicyNets.GMMActorNetwork( + obs_shapes=self.obs_shapes, + goal_shapes=self.goal_shapes, + ac_dim=self.ac_dim, + mlp_layer_dims=self.algo_config.actor_layer_dims, + num_modes=self.algo_config.gmm.num_modes, + min_std=self.algo_config.gmm.min_std, + std_activation=self.algo_config.gmm.std_activation, + low_noise_eval=self.algo_config.gmm.low_noise_eval, + encoder_kwargs=ObsUtils.obs_encoder_kwargs_from_config(self.obs_config.encoder), + ) + + self.nets = self.nets.float().to(self.device) + + +class BC_VAE(BC): + """ + BC training with a VAE policy. + """ + def _create_networks(self): + """ + Creates networks and places them into @self.nets. + """ + self.nets = nn.ModuleDict() + self.nets["policy"] = PolicyNets.VAEActor( + obs_shapes=self.obs_shapes, + goal_shapes=self.goal_shapes, + ac_dim=self.ac_dim, + device=self.device, + encoder_kwargs=ObsUtils.obs_encoder_kwargs_from_config(self.obs_config.encoder), + **VAENets.vae_args_from_config(self.algo_config.vae), + ) + + self.nets = self.nets.float().to(self.device) + + def train_on_batch(self, batch, epoch, validate=False): + """ + Update from superclass to set categorical temperature, for categorical VAEs. + """ + if self.algo_config.vae.prior.use_categorical: + temperature = self.algo_config.vae.prior.categorical_init_temp - epoch * self.algo_config.vae.prior.categorical_temp_anneal_step + temperature = max(temperature, self.algo_config.vae.prior.categorical_min_temp) + self.nets["policy"].set_gumbel_temperature(temperature) + return super(BC_VAE, self).train_on_batch(batch, epoch, validate=validate) + + def _forward_training(self, batch): + """ + Internal helper function for BC algo class. Compute forward pass + and return network outputs in @predictions dict. + + Args: + batch (dict): dictionary with torch.Tensors sampled + from a data loader and filtered by @process_batch_for_training + + Returns: + predictions (dict): dictionary containing network outputs + """ + vae_inputs = dict( + actions=batch["actions"], + obs_dict=batch["obs"], + goal_dict=batch["goal_obs"], + freeze_encoder=batch.get("freeze_encoder", False), + ) + + vae_outputs = self.nets["policy"].forward_train(**vae_inputs) + predictions = OrderedDict( + actions=vae_outputs["decoder_outputs"], + kl_loss=vae_outputs["kl_loss"], + reconstruction_loss=vae_outputs["reconstruction_loss"], + encoder_z=vae_outputs["encoder_z"], + ) + if not self.algo_config.vae.prior.use_categorical: + with torch.no_grad(): + encoder_variance = torch.exp(vae_outputs["encoder_params"]["logvar"]) + predictions["encoder_variance"] = encoder_variance + return predictions + + def _compute_losses(self, predictions, batch): + """ + Internal helper function for BC algo class. Compute losses based on + network outputs in @predictions dict, using reference labels in @batch. + + Args: + predictions (dict): dictionary containing network outputs, from @_forward_training + batch (dict): dictionary with torch.Tensors sampled + from a data loader and filtered by @process_batch_for_training + + Returns: + losses (dict): dictionary of losses computed over the batch + """ + + # total loss is sum of reconstruction and KL, weighted by beta + kl_loss = predictions["kl_loss"] + recons_loss = predictions["reconstruction_loss"] + action_loss = recons_loss + self.algo_config.vae.kl_weight * kl_loss + return OrderedDict( + recons_loss=recons_loss, + kl_loss=kl_loss, + action_loss=action_loss, + ) + + def log_info(self, info): + """ + Process info dictionary from @train_on_batch to summarize + information to pass to tensorboard for logging. + + Args: + info (dict): dictionary of info + + Returns: + loss_log (dict): name -> summary statistic + """ + log = PolicyAlgo.log_info(self, info) + log["Loss"] = info["losses"]["action_loss"].item() + log["KL_Loss"] = info["losses"]["kl_loss"].item() + log["Reconstruction_Loss"] = info["losses"]["recons_loss"].item() + if self.algo_config.vae.prior.use_categorical: + log["Gumbel_Temperature"] = self.nets["policy"].get_gumbel_temperature() + else: + log["Encoder_Variance"] = info["predictions"]["encoder_variance"].mean().item() + if "policy_grad_norms" in info: + log["Policy_Grad_Norms"] = info["policy_grad_norms"] + return log + + +class BC_RNN(BC): + """ + BC training with an RNN policy. + """ + def _create_networks(self): + """ + Creates networks and places them into @self.nets. + """ + self.nets = nn.ModuleDict() + self.nets["policy"] = PolicyNets.RNNActorNetwork( + obs_shapes=self.obs_shapes, + goal_shapes=self.goal_shapes, + ac_dim=self.ac_dim, + mlp_layer_dims=self.algo_config.actor_layer_dims, + encoder_kwargs=ObsUtils.obs_encoder_kwargs_from_config(self.obs_config.encoder), + **BaseNets.rnn_args_from_config(self.algo_config.rnn), + ) + + self._rnn_hidden_state = None + self._rnn_horizon = self.algo_config.rnn.horizon + self._rnn_counter = 0 + self._rnn_is_open_loop = self.algo_config.rnn.get("open_loop", False) + + self.nets = self.nets.float().to(self.device) + + def process_batch_for_training(self, batch): + """ + Processes input batch from a data loader to filter out + relevant information and prepare the batch for training. + + Args: + batch (dict): dictionary with torch.Tensors sampled + from a data loader + + Returns: + input_batch (dict): processed and filtered batch that + will be used for training + """ + input_batch = dict() + input_batch["obs"] = batch["obs"] + input_batch["goal_obs"] = batch.get("goal_obs", None) # goals may not be present + input_batch["actions"] = batch["actions"] + + if self._rnn_is_open_loop: + # replace the observation sequence with one that only consists of the first observation. + # This way, all actions are predicted "open-loop" after the first observation, based + # on the rnn hidden state. + n_steps = batch["actions"].shape[1] + obs_seq_start = TensorUtils.index_at_time(batch["obs"], ind=0) + input_batch["obs"] = TensorUtils.unsqueeze_expand_at(obs_seq_start, size=n_steps, dim=1) + + # we move to device first before float conversion because image observation modalities will be uint8 - + # this minimizes the amount of data transferred to GPU + return TensorUtils.to_float(TensorUtils.to_device(input_batch, self.device)) + + def get_action(self, obs_dict, goal_dict=None): + """ + Get policy action outputs. + + Args: + obs_dict (dict): current observation + goal_dict (dict): (optional) goal + + Returns: + action (torch.Tensor): action tensor + """ + assert not self.nets.training + + if self._rnn_hidden_state is None or self._rnn_counter % self._rnn_horizon == 0: + batch_size = list(obs_dict.values())[0].shape[0] + self._rnn_hidden_state = self.nets["policy"].get_rnn_init_state(batch_size=batch_size, device=self.device) + + if self._rnn_is_open_loop: + # remember the initial observation, and use it instead of the current observation + # for open-loop action sequence prediction + self._open_loop_obs = TensorUtils.clone(TensorUtils.detach(obs_dict)) + + obs_to_use = obs_dict + if self._rnn_is_open_loop: + # replace current obs with last recorded obs + obs_to_use = self._open_loop_obs + + self._rnn_counter += 1 + action, self._rnn_hidden_state = self.nets["policy"].forward_step( + obs_to_use, goal_dict=goal_dict, rnn_state=self._rnn_hidden_state) + return action + + def reset(self): + """ + Reset algo state to prepare for environment rollouts. + """ + self._rnn_hidden_state = None + self._rnn_counter = 0 + + +class BC_RNN_GMM(BC_RNN): + """ + BC training with an RNN GMM policy. + """ + def _create_networks(self): + """ + Creates networks and places them into @self.nets. + """ + assert self.algo_config.gmm.enabled + assert self.algo_config.rnn.enabled + + self.nets = nn.ModuleDict() + self.nets["policy"] = PolicyNets.RNNGMMActorNetwork( + obs_shapes=self.obs_shapes, + goal_shapes=self.goal_shapes, + ac_dim=self.ac_dim, + mlp_layer_dims=self.algo_config.actor_layer_dims, + num_modes=self.algo_config.gmm.num_modes, + min_std=self.algo_config.gmm.min_std, + std_activation=self.algo_config.gmm.std_activation, + low_noise_eval=self.algo_config.gmm.low_noise_eval, + encoder_kwargs=ObsUtils.obs_encoder_kwargs_from_config(self.obs_config.encoder), + **BaseNets.rnn_args_from_config(self.algo_config.rnn), + ) + + self._rnn_hidden_state = None + self._rnn_horizon = self.algo_config.rnn.horizon + self._rnn_counter = 0 + self._rnn_is_open_loop = self.algo_config.rnn.get("open_loop", False) + + self.nets = self.nets.float().to(self.device) + + def _forward_training(self, batch): + """ + Internal helper function for BC algo class. Compute forward pass + and return network outputs in @predictions dict. + + Args: + batch (dict): dictionary with torch.Tensors sampled + from a data loader and filtered by @process_batch_for_training + + Returns: + predictions (dict): dictionary containing network outputs + """ + dists = self.nets["policy"].forward_train( + obs_dict=batch["obs"], + goal_dict=batch["goal_obs"], + ) + + # make sure that this is a batch of multivariate action distributions, so that + # the log probability computation will be correct + assert len(dists.batch_shape) == 2 # [B, T] + log_probs = dists.log_prob(batch["actions"]) + + predictions = OrderedDict( + log_probs=log_probs, + ) + return predictions + + def _compute_losses(self, predictions, batch): + """ + Internal helper function for BC algo class. Compute losses based on + network outputs in @predictions dict, using reference labels in @batch. + + Args: + predictions (dict): dictionary containing network outputs, from @_forward_training + batch (dict): dictionary with torch.Tensors sampled + from a data loader and filtered by @process_batch_for_training + + Returns: + losses (dict): dictionary of losses computed over the batch + """ + + # loss is just negative log-likelihood of action targets + action_loss = -predictions["log_probs"].mean() + return OrderedDict( + log_probs=-action_loss, + action_loss=action_loss, + ) + + def log_info(self, info): + """ + Process info dictionary from @train_on_batch to summarize + information to pass to tensorboard for logging. + + Args: + info (dict): dictionary of info + + Returns: + loss_log (dict): name -> summary statistic + """ + log = PolicyAlgo.log_info(self, info) + log["Loss"] = info["losses"]["action_loss"].item() + log["Log_Likelihood"] = info["losses"]["log_probs"].item() + if "policy_grad_norms" in info: + log["Policy_Grad_Norms"] = info["policy_grad_norms"] + return log + + +class BC_Transformer(BC): + """ + BC training with a Transformer policy. + """ + def _create_networks(self): + """ + Creates networks and places them into @self.nets. + """ + assert self.algo_config.transformer.enabled + + self.nets = nn.ModuleDict() + self.nets["policy"] = PolicyNets.TransformerActorNetwork( + obs_shapes=self.obs_shapes, + goal_shapes=self.goal_shapes, + ac_dim=self.ac_dim, + encoder_kwargs=ObsUtils.obs_encoder_kwargs_from_config(self.obs_config.encoder), + **BaseNets.transformer_args_from_config(self.algo_config.transformer), + ) + self._set_params_from_config() + self.nets = self.nets.float().to(self.device) + + def _set_params_from_config(self): + """ + Read specific config variables we need for training / eval. + Called by @_create_networks method + """ + self.context_length = self.algo_config.transformer.context_length + self.supervise_all_steps = self.algo_config.transformer.supervise_all_steps + + def process_batch_for_training(self, batch): + """ + Processes input batch from a data loader to filter out + relevant information and prepare the batch for training. + Args: + batch (dict): dictionary with torch.Tensors sampled + from a data loader + Returns: + input_batch (dict): processed and filtered batch that + will be used for training + """ + input_batch = dict() + h = self.context_length + input_batch["obs"] = {k: batch["obs"][k][:, :h, :] for k in batch["obs"]} + input_batch["goal_obs"] = batch.get("goal_obs", None) # goals may not be present + + if self.supervise_all_steps: + # supervision on entire sequence (instead of just current timestep) + input_batch["actions"] = batch["actions"][:, :h, :] + else: + # just use current timestep + input_batch["actions"] = batch["actions"][:, h-1, :] + + input_batch = TensorUtils.to_device(TensorUtils.to_float(input_batch), self.device) + return input_batch + + def _forward_training(self, batch, epoch=None): + """ + Internal helper function for BC_Transformer algo class. Compute forward pass + and return network outputs in @predictions dict. + + Args: + batch (dict): dictionary with torch.Tensors sampled + from a data loader and filtered by @process_batch_for_training + + Returns: + predictions (dict): dictionary containing network outputs + """ + # ensure that transformer context length is consistent with temporal dimension of observations + TensorUtils.assert_size_at_dim( + batch["obs"], + size=(self.context_length), + dim=1, + msg="Error: expect temporal dimension of obs batch to match transformer context length {}".format(self.context_length), + ) + + predictions = OrderedDict() + predictions["actions"] = self.nets["policy"](obs_dict=batch["obs"], actions=None, goal_dict=batch["goal_obs"]) + if not self.supervise_all_steps: + # only supervise final timestep + predictions["actions"] = predictions["actions"][:, -1, :] + return predictions + + def get_action(self, obs_dict, goal_dict=None): + """ + Get policy action outputs. + Args: + obs_dict (dict): current observation + goal_dict (dict): (optional) goal + Returns: + action (torch.Tensor): action tensor + """ + assert not self.nets.training + + return self.nets["policy"](obs_dict, actions=None, goal_dict=goal_dict)[:, -1, :] + + +class BC_Transformer_GMM(BC_Transformer): + """ + BC training with a Transformer GMM policy. + """ + def _create_networks(self): + """ + Creates networks and places them into @self.nets. + """ + assert self.algo_config.gmm.enabled + assert self.algo_config.transformer.enabled + + self.nets = nn.ModuleDict() + self.nets["policy"] = PolicyNets.TransformerGMMActorNetwork( + obs_shapes=self.obs_shapes, + goal_shapes=self.goal_shapes, + ac_dim=self.ac_dim, + num_modes=self.algo_config.gmm.num_modes, + min_std=self.algo_config.gmm.min_std, + std_activation=self.algo_config.gmm.std_activation, + low_noise_eval=self.algo_config.gmm.low_noise_eval, + encoder_kwargs=ObsUtils.obs_encoder_kwargs_from_config(self.obs_config.encoder), + **BaseNets.transformer_args_from_config(self.algo_config.transformer), + ) + self._set_params_from_config() + self.nets = self.nets.float().to(self.device) + + def _forward_training(self, batch, epoch=None): + """ + Modify from super class to support GMM training. + """ + # ensure that transformer context length is consistent with temporal dimension of observations + TensorUtils.assert_size_at_dim( + batch["obs"], + size=(self.context_length), + dim=1, + msg="Error: expect temporal dimension of obs batch to match transformer context length {}".format(self.context_length), + ) + + dists = self.nets["policy"].forward_train( + obs_dict=batch["obs"], + actions=None, + goal_dict=batch["goal_obs"], + low_noise_eval=False, + ) + + # make sure that this is a batch of multivariate action distributions, so that + # the log probability computation will be correct + assert len(dists.batch_shape) == 2 # [B, T] + + if not self.supervise_all_steps: + # only use final timestep prediction by making a new distribution with only final timestep. + # This essentially does `dists = dists[:, -1]` + component_distribution = D.Normal( + loc=dists.component_distribution.base_dist.loc[:, -1], + scale=dists.component_distribution.base_dist.scale[:, -1], + ) + component_distribution = D.Independent(component_distribution, 1) + mixture_distribution = D.Categorical(logits=dists.mixture_distribution.logits[:, -1]) + dists = D.MixtureSameFamily( + mixture_distribution=mixture_distribution, + component_distribution=component_distribution, + ) + + log_probs = dists.log_prob(batch["actions"]) + + predictions = OrderedDict( + log_probs=log_probs, + ) + return predictions + + def _compute_losses(self, predictions, batch): + """ + Internal helper function for BC_Transformer_GMM algo class. Compute losses based on + network outputs in @predictions dict, using reference labels in @batch. + Args: + predictions (dict): dictionary containing network outputs, from @_forward_training + batch (dict): dictionary with torch.Tensors sampled + from a data loader and filtered by @process_batch_for_training + Returns: + losses (dict): dictionary of losses computed over the batch + """ + + # loss is just negative log-likelihood of action targets + action_loss = -predictions["log_probs"].mean() + return OrderedDict( + log_probs=-action_loss, + action_loss=action_loss, + ) + + def log_info(self, info): + """ + Process info dictionary from @train_on_batch to summarize + information to pass to tensorboard for logging. + Args: + info (dict): dictionary of info + Returns: + loss_log (dict): name -> summary statistic + """ + log = PolicyAlgo.log_info(self, info) + log["Loss"] = info["losses"]["action_loss"].item() + log["Log_Likelihood"] = info["losses"]["log_probs"].item() + if "policy_grad_norms" in info: + log["Policy_Grad_Norms"] = info["policy_grad_norms"] + return log + + +#### Laura Mods here +class BC_RNN_GAUSSIAN(BC_RNN): + """ + BC training with an RNN Gaussian policy (Heteroscedastic Uncertainty). + """ + def _create_networks(self): + """ + Creates networks and places them into @self.nets. + """ + assert self.algo_config.gaussian.enabled + assert self.algo_config.rnn.enabled + + self.nets = nn.ModuleDict() + + # NOTE: This requires a new network class in robomimic.models.policy_nets + # that implements an RNN-based policy which outputs the parameters + # (mean and log_std) of a Gaussian distribution for a sequence. + self.nets["policy"] = PolicyNets.GaussianRNNActorNetwork( + obs_shapes=self.obs_shapes, + goal_shapes=self.goal_shapes, + ac_dim=self.ac_dim, + mlp_layer_dims=self.algo_config.actor_layer_dims, + fixed_std=self.algo_config.gaussian.fixed_std, + init_std=self.algo_config.gaussian.init_std, + std_limits=(self.algo_config.gaussian.min_std, 7.5), + std_activation=self.algo_config.gaussian.std_activation, + low_noise_eval=self.algo_config.gaussian.low_noise_eval, + encoder_kwargs=ObsUtils.obs_encoder_kwargs_from_config(self.obs_config.encoder), + **BaseNets.rnn_args_from_config(self.algo_config.rnn), + ) + + self._rnn_hidden_state = None + self._rnn_horizon = self.algo_config.rnn.horizon + self._rnn_counter = 0 + self._rnn_is_open_loop = self.algo_config.rnn.get("open_loop", False) + + self.nets = self.nets.float().to(self.device) + + def _forward_training(self, batch): + """ + Internal helper function to compute forward pass and return log-probabilities. + """ + # Policy is a GaussianRNNActorNetwork that outputs a torch.Distribution object (sequence of distributions). + dists = self.nets["policy"].forward_train( + obs_dict=batch["obs"], + goal_dict=batch["goal_obs"], + ) + + # The distribution must be a batch of action sequence distributions: [B, T] + assert len(dists.batch_shape) == 2 + + # Calculate log probability for the entire sequence of ground truth actions + log_probs = dists.log_prob(batch["actions"]) + + predictions = OrderedDict( + log_probs=log_probs, + ) + return predictions + + def _compute_losses(self, predictions, batch): + """ + Loss is the Gaussian Negative Log-Likelihood (NLL). + """ + # Loss is the negative mean log-likelihood of action targets across B x T + action_loss = -predictions["log_probs"].mean() + return OrderedDict( + log_probs=-action_loss, + action_loss=action_loss, + ) + + def log_info(self, info): + """ + Update log information to include NLL loss. + """ + log = PolicyAlgo.log_info(self, info) + log["Loss"] = info["losses"]["action_loss"].item() + log["Log_Likelihood"] = info["losses"]["log_probs"].item() + if "policy_grad_norms" in info: + log["Policy_Grad_Norms"] = info["policy_grad_norms"] + return log + + def get_action(self, obs_dict, goal_dict=None): + """ + Overrides BC_RNN.get_action(). + Get action using the mean of the predicted Gaussian distribution. + """ + assert not self.nets.training + + if self._rnn_hidden_state is None or self._rnn_counter % self._rnn_horizon == 0: + batch_size = list(obs_dict.values())[0].shape[0] + self._rnn_hidden_state = self.nets["policy"].get_rnn_init_state(batch_size=batch_size, device=self.device) + + if self._rnn_is_open_loop: + # remember the initial observation, and use it instead of the current observation + self._open_loop_obs = TensorUtils.clone(TensorUtils.detach(obs_dict)) + + obs_to_use = obs_dict + if self._rnn_is_open_loop: + # replace current obs with last recorded obs + obs_to_use = self._open_loop_obs + + self._rnn_counter += 1 + + # Use forward_step which should return the mean action and the updated RNN state + action, self._rnn_hidden_state = self.nets["policy"].forward_step( + obs_to_use, goal_dict=goal_dict, rnn_state=self._rnn_hidden_state) + + return action \ No newline at end of file diff --git a/docs/scripts/policy_nets.py b/docs/scripts/policy_nets.py new file mode 100644 index 000000000000..8dba1d934cbb --- /dev/null +++ b/docs/scripts/policy_nets.py @@ -0,0 +1,1570 @@ +""" +Contains torch Modules for policy networks. These networks take an +observation dictionary as input (and possibly additional conditioning, +such as subgoal or goal dictionaries) and produce action predictions, +samples, or distributions as outputs. Note that actions +are assumed to lie in [-1, 1], and most networks will have a final +tanh activation to help ensure this range. +""" +import textwrap +import numpy as np +from collections import OrderedDict + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.distributions as D + +import robomimic.utils.tensor_utils as TensorUtils +from robomimic.models.base_nets import Module +from robomimic.models.transformers import GPT_Backbone +from robomimic.models.obs_nets import MIMO_MLP, RNN_MIMO_MLP, MIMO_Transformer, ObservationDecoder +from robomimic.models.vae_nets import VAE +from robomimic.models.distributions import TanhWrappedDistribution + + +class ActorNetwork(MIMO_MLP): + """ + A basic policy network that predicts actions from observations. + Can optionally be goal conditioned on future observations. + """ + def __init__( + self, + obs_shapes, + ac_dim, + mlp_layer_dims, + goal_shapes=None, + encoder_kwargs=None, + ): + """ + Args: + obs_shapes (OrderedDict): a dictionary that maps observation keys to + expected shapes for observations. + + ac_dim (int): dimension of action space. + + mlp_layer_dims ([int]): sequence of integers for the MLP hidden layers sizes. + + goal_shapes (OrderedDict): a dictionary that maps observation keys to + expected shapes for goal observations. + + encoder_kwargs (dict or None): If None, results in default encoder_kwargs being applied. Otherwise, should + be nested dictionary containing relevant per-observation key information for encoder networks. + Should be of form: + + obs_modality1: dict + feature_dimension: int + core_class: str + core_kwargs: dict + ... + ... + obs_randomizer_class: str + obs_randomizer_kwargs: dict + ... + ... + obs_modality2: dict + ... + """ + assert isinstance(obs_shapes, OrderedDict) + self.obs_shapes = obs_shapes + self.ac_dim = ac_dim + + # set up different observation groups for @MIMO_MLP + observation_group_shapes = OrderedDict() + observation_group_shapes["obs"] = OrderedDict(self.obs_shapes) + + self._is_goal_conditioned = False + if goal_shapes is not None and len(goal_shapes) > 0: + assert isinstance(goal_shapes, OrderedDict) + self._is_goal_conditioned = True + self.goal_shapes = OrderedDict(goal_shapes) + observation_group_shapes["goal"] = OrderedDict(self.goal_shapes) + else: + self.goal_shapes = OrderedDict() + + output_shapes = self._get_output_shapes() + super(ActorNetwork, self).__init__( + input_obs_group_shapes=observation_group_shapes, + output_shapes=output_shapes, + layer_dims=mlp_layer_dims, + encoder_kwargs=encoder_kwargs, + ) + + def _get_output_shapes(self): + """ + Allow subclasses to re-define outputs from @MIMO_MLP, since we won't + always directly predict actions, but may instead predict the parameters + of a action distribution. + """ + return OrderedDict(action=(self.ac_dim,)) + + def output_shape(self, input_shape=None): + return [self.ac_dim] + + def forward(self, obs_dict, goal_dict=None): + actions = super(ActorNetwork, self).forward(obs=obs_dict, goal=goal_dict)["action"] + # apply tanh squashing to ensure actions are in [-1, 1] + return torch.tanh(actions) + + def _to_string(self): + """Info to pretty print.""" + return "action_dim={}".format(self.ac_dim) + + +class PerturbationActorNetwork(ActorNetwork): + """ + An action perturbation network - primarily used in BCQ. + It takes states and actions and returns action perturbations. + """ + def __init__( + self, + obs_shapes, + ac_dim, + mlp_layer_dims, + perturbation_scale=0.05, + goal_shapes=None, + encoder_kwargs=None, + ): + """ + Args: + obs_shapes (OrderedDict): a dictionary that maps observation keys to + expected shapes for observations. + + ac_dim (int): dimension of action space. + + mlp_layer_dims ([int]): sequence of integers for the MLP hidden layers sizes. + + perturbation_scale (float): the perturbation network output is always squashed to + lie in +/- @perturbation_scale. The final action output is equal to the original + input action added to the output perturbation (and clipped to lie in [-1, 1]). + + goal_shapes (OrderedDict): a dictionary that maps modality to + expected shapes for goal observations. + + encoder_kwargs (dict or None): If None, results in default encoder_kwargs being applied. Otherwise, should + be nested dictionary containing relevant per-modality information for encoder networks. + Should be of form: + + obs_modality1: dict + feature_dimension: int + core_class: str + core_kwargs: dict + ... + ... + obs_randomizer_class: str + obs_randomizer_kwargs: dict + ... + ... + obs_modality2: dict + ... + """ + self.perturbation_scale = perturbation_scale + + # add in action as a modality + new_obs_shapes = OrderedDict(obs_shapes) + new_obs_shapes["action"] = (ac_dim,) + + # pass to super class to instantiate network + super(PerturbationActorNetwork, self).__init__( + obs_shapes=new_obs_shapes, + ac_dim=ac_dim, + mlp_layer_dims=mlp_layer_dims, + goal_shapes=goal_shapes, + encoder_kwargs=encoder_kwargs, + ) + + def forward(self, obs_dict, acts, goal_dict=None): + """Forward pass through perturbation actor.""" + # add in actions + inputs = dict(obs_dict) + inputs["action"] = acts + perturbations = super(PerturbationActorNetwork, self).forward(inputs, goal_dict) + + # add perturbations from network to original actions, and ensure the new actions lie in [-1, 1] + output_actions = acts + self.perturbation_scale * perturbations + output_actions = output_actions.clamp(-1.0, 1.0) + return output_actions + + def _to_string(self): + """Info to pretty print.""" + return "action_dim={}, perturbation_scale={}".format(self.ac_dim, self.perturbation_scale) + + +class GaussianActorNetwork(ActorNetwork): + """ + Variant of actor network that learns a diagonal unimodal Gaussian distribution + over actions. + """ + def __init__( + self, + obs_shapes, + ac_dim, + mlp_layer_dims, + fixed_std=False, + std_activation="softplus", + init_last_fc_weight=None, + init_std=0.3, + mean_limits=(-9.0, 9.0), + std_limits=(0.007, 7.5), + low_noise_eval=True, + use_tanh=False, + goal_shapes=None, + encoder_kwargs=None, + ): + """ + Args: + obs_shapes (OrderedDict): a dictionary that maps modality to + expected shapes for observations. + + ac_dim (int): dimension of action space. + + mlp_layer_dims ([int]): sequence of integers for the MLP hidden layers sizes. + + fixed_std (bool): if True, std is not learned, but kept constant at @init_std + + std_activation (None or str): type of activation to use for std deviation. Options are: + + None: no activation applied (not recommended unless using fixed std) + + `'softplus'`: Only applicable if not using fixed std. Softplus activation applied, after which the + output is scaled by init_std / softplus(0) + + `'exp'`: Only applicable if not using fixed std. Exp applied; this corresponds to network output + as being interpreted as log_std instead of std + + NOTE: In all cases, the final result is clipped to be within @std_limits + + init_last_fc_weight (None or float): if specified, will intialize the final layer network weights to be + uniformly sampled from [-init_weight, init_weight] + + init_std (None or float): approximate initial scaling for standard deviation outputs + from network. If None + + mean_limits (2-array): (min, max) to clamp final mean output by + + std_limits (2-array): (min, max) to clamp final std output by + + low_noise_eval (float): if True, model will output means of Gaussian distribution + at eval time. + + use_tanh (bool): if True, use a tanh-Gaussian distribution + + goal_shapes (OrderedDict): a dictionary that maps modality to + expected shapes for goal observations. + + encoder_kwargs (dict or None): If None, results in default encoder_kwargs being applied. Otherwise, should + be nested dictionary containing relevant per-modality information for encoder networks. + Should be of form: + + obs_modality1: dict + feature_dimension: int + core_class: str + core_kwargs: dict + ... + ... + obs_randomizer_class: str + obs_randomizer_kwargs: dict + ... + ... + obs_modality2: dict + ... + """ + + # parameters specific to Gaussian actor + self.fixed_std = fixed_std + self.init_std = init_std + self.mean_limits = np.array(mean_limits) + self.std_limits = np.array(std_limits) + + # Define activations to use + def softplus_scaled(x): + out = F.softplus(x) + out = out * (self.init_std / F.softplus(torch.zeros(1).to(x.device))) + return out + + self.activations = { + None: lambda x: x, + "softplus": softplus_scaled, + "exp": torch.exp, + } + assert std_activation in self.activations, \ + "std_activation must be one of: {}; instead got: {}".format(self.activations.keys(), std_activation) + self.std_activation = std_activation if not self.fixed_std else None + + self.low_noise_eval = low_noise_eval + self.use_tanh = use_tanh + + super(GaussianActorNetwork, self).__init__( + obs_shapes=obs_shapes, + ac_dim=ac_dim, + mlp_layer_dims=mlp_layer_dims, + goal_shapes=goal_shapes, + encoder_kwargs=encoder_kwargs, + ) + + # If initialization weight was specified, make sure all final layer network weights are specified correctly + if init_last_fc_weight is not None: + with torch.no_grad(): + for name, layer in self.nets["decoder"].nets.items(): + torch.nn.init.uniform_(layer.weight, -init_last_fc_weight, init_last_fc_weight) + torch.nn.init.uniform_(layer.bias, -init_last_fc_weight, init_last_fc_weight) + + def _get_output_shapes(self): + """ + Tells @MIMO_MLP superclass about the output dictionary that should be generated + at the last layer. Network outputs parameters of Gaussian distribution. + """ + return OrderedDict( + mean=(self.ac_dim,), + scale=(self.ac_dim,), + ) + + def forward_train(self, obs_dict, goal_dict=None): + """ + Return full Gaussian distribution, which is useful for computing + quantities necessary at train-time, like log-likelihood, KL + divergence, etc. + + Args: + obs_dict (dict): batch of observations + goal_dict (dict): if not None, batch of goal observations + + Returns: + dist (Distribution): Gaussian distribution + """ + out = MIMO_MLP.forward(self, obs=obs_dict, goal=goal_dict) + mean = out["mean"] + # Use either constant std or learned std depending on setting + scale = out["scale"] if not self.fixed_std else torch.ones_like(mean) * self.init_std + + # Clamp the mean + mean = torch.clamp(mean, min=self.mean_limits[0], max=self.mean_limits[1]) + + # apply tanh squashing to mean if not using tanh-Gaussian to ensure mean is in [-1, 1] + if not self.use_tanh: + mean = torch.tanh(mean) + + # Calculate scale + if self.low_noise_eval and (not self.training): + # override std value so that you always approximately sample the mean + scale = torch.ones_like(mean) * 1e-4 + else: + # Post-process the scale accordingly + scale = self.activations[self.std_activation](scale) + # Clamp the scale + scale = torch.clamp(scale, min=self.std_limits[0], max=self.std_limits[1]) + + + # the Independent call will make it so that `batch_shape` for dist will be equal to batch size + # while `event_shape` will be equal to action dimension - ensuring that log-probability + # computations are summed across the action dimension + dist = D.Normal(loc=mean, scale=scale) + dist = D.Independent(dist, 1) + + if self.use_tanh: + # Wrap distribution with Tanh + dist = TanhWrappedDistribution(base_dist=dist, scale=1.) + + return dist + + def forward(self, obs_dict, goal_dict=None): + """ + Samples actions from the policy distribution. + + Args: + obs_dict (dict): batch of observations + goal_dict (dict): if not None, batch of goal observations + + Returns: + action (torch.Tensor): batch of actions from policy distribution + """ + dist = self.forward_train(obs_dict, goal_dict) + if self.low_noise_eval and (not self.training): + if self.use_tanh: + # # scaling factor lets us output actions like [-1. 1.] and is consistent with the distribution transform + # return (1. + 1e-6) * torch.tanh(dist.base_dist.mean) + return torch.tanh(dist.mean) + return dist.mean + return dist.sample() + + def _to_string(self): + """Info to pretty print.""" + msg = "action_dim={}\nfixed_std={}\nstd_activation={}\ninit_std={}\nmean_limits={}\nstd_limits={}\nlow_noise_eval={}".format( + self.ac_dim, self.fixed_std, self.std_activation, self.init_std, self.mean_limits, self.std_limits, self.low_noise_eval) + return msg + + +class GMMActorNetwork(ActorNetwork): + """ + Variant of actor network that learns a multimodal Gaussian mixture distribution + over actions. + """ + def __init__( + self, + obs_shapes, + ac_dim, + mlp_layer_dims, + num_modes=5, + min_std=0.01, + std_activation="softplus", + low_noise_eval=True, + use_tanh=False, + goal_shapes=None, + encoder_kwargs=None, + ): + """ + Args: + obs_shapes (OrderedDict): a dictionary that maps modality to + expected shapes for observations. + + ac_dim (int): dimension of action space. + + mlp_layer_dims ([int]): sequence of integers for the MLP hidden layers sizes. + + num_modes (int): number of GMM modes + + min_std (float): minimum std output from network + + std_activation (None or str): type of activation to use for std deviation. Options are: + + `'softplus'`: Softplus activation applied + + `'exp'`: Exp applied; this corresponds to network output being interpreted as log_std instead of std + + low_noise_eval (float): if True, model will sample from GMM with low std, so that + one of the GMM modes will be sampled (approximately) + + use_tanh (bool): if True, use a tanh-Gaussian distribution + + goal_shapes (OrderedDict): a dictionary that maps modality to + expected shapes for goal observations. + + encoder_kwargs (dict or None): If None, results in default encoder_kwargs being applied. Otherwise, should + be nested dictionary containing relevant per-modality information for encoder networks. + Should be of form: + + obs_modality1: dict + feature_dimension: int + core_class: str + core_kwargs: dict + ... + ... + obs_randomizer_class: str + obs_randomizer_kwargs: dict + ... + ... + obs_modality2: dict + ... + """ + + # parameters specific to GMM actor + self.num_modes = num_modes + self.min_std = min_std + self.low_noise_eval = low_noise_eval + self.use_tanh = use_tanh + + # Define activations to use + self.activations = { + "softplus": F.softplus, + "exp": torch.exp, + } + assert std_activation in self.activations, \ + "std_activation must be one of: {}; instead got: {}".format(self.activations.keys(), std_activation) + self.std_activation = std_activation + + super(GMMActorNetwork, self).__init__( + obs_shapes=obs_shapes, + ac_dim=ac_dim, + mlp_layer_dims=mlp_layer_dims, + goal_shapes=goal_shapes, + encoder_kwargs=encoder_kwargs, + ) + + def _get_output_shapes(self): + """ + Tells @MIMO_MLP superclass about the output dictionary that should be generated + at the last layer. Network outputs parameters of GMM distribution. + """ + return OrderedDict( + mean=(self.num_modes, self.ac_dim), + scale=(self.num_modes, self.ac_dim), + logits=(self.num_modes,), + ) + + def forward_train(self, obs_dict, goal_dict=None): + """ + Return full GMM distribution, which is useful for computing + quantities necessary at train-time, like log-likelihood, KL + divergence, etc. + + Args: + obs_dict (dict): batch of observations + goal_dict (dict): if not None, batch of goal observations + + Returns: + dist (Distribution): GMM distribution + """ + out = MIMO_MLP.forward(self, obs=obs_dict, goal=goal_dict) + means = out["mean"] + scales = out["scale"] + logits = out["logits"] + + # apply tanh squashing to means if not using tanh-GMM to ensure means are in [-1, 1] + if not self.use_tanh: + means = torch.tanh(means) + + # Calculate scale + if self.low_noise_eval and (not self.training): + # low-noise for all Gaussian dists + scales = torch.ones_like(means) * 1e-4 + else: + # post-process the scale accordingly + scales = self.activations[self.std_activation](scales) + self.min_std + + # mixture components - make sure that `batch_shape` for the distribution is equal + # to (batch_size, num_modes) since MixtureSameFamily expects this shape + component_distribution = D.Normal(loc=means, scale=scales) + component_distribution = D.Independent(component_distribution, 1) + + # unnormalized logits to categorical distribution for mixing the modes + mixture_distribution = D.Categorical(logits=logits) + + dist = D.MixtureSameFamily( + mixture_distribution=mixture_distribution, + component_distribution=component_distribution, + ) + + if self.use_tanh: + # Wrap distribution with Tanh + dist = TanhWrappedDistribution(base_dist=dist, scale=1.) + + return dist + + def forward(self, obs_dict, goal_dict=None): + """ + Samples actions from the policy distribution. + + Args: + obs_dict (dict): batch of observations + goal_dict (dict): if not None, batch of goal observations + + Returns: + action (torch.Tensor): batch of actions from policy distribution + """ + dist = self.forward_train(obs_dict, goal_dict) + return dist.sample() + + def _to_string(self): + """Info to pretty print.""" + return "action_dim={}\nnum_modes={}\nmin_std={}\nstd_activation={}\nlow_noise_eval={}".format( + self.ac_dim, self.num_modes, self.min_std, self.std_activation, self.low_noise_eval) + + +class RNNActorNetwork(RNN_MIMO_MLP): + """ + An RNN policy network that predicts actions from observations. + """ + def __init__( + self, + obs_shapes, + ac_dim, + mlp_layer_dims, + rnn_hidden_dim, + rnn_num_layers, + rnn_type="LSTM", # [LSTM, GRU] + rnn_kwargs=None, + goal_shapes=None, + encoder_kwargs=None, + ): + """ + Args: + obs_shapes (OrderedDict): a dictionary that maps modality to + expected shapes for observations. + + ac_dim (int): dimension of action space. + + mlp_layer_dims ([int]): sequence of integers for the MLP hidden layers sizes. + + rnn_hidden_dim (int): RNN hidden dimension + + rnn_num_layers (int): number of RNN layers + + rnn_type (str): [LSTM, GRU] + + rnn_kwargs (dict): kwargs for the torch.nn.LSTM / GRU + + goal_shapes (OrderedDict): a dictionary that maps modality to + expected shapes for goal observations. + + encoder_kwargs (dict or None): If None, results in default encoder_kwargs being applied. Otherwise, should + be nested dictionary containing relevant per-modality information for encoder networks. + Should be of form: + + obs_modality1: dict + feature_dimension: int + core_class: str + core_kwargs: dict + ... + ... + obs_randomizer_class: str + obs_randomizer_kwargs: dict + ... + ... + obs_modality2: dict + ... + """ + self.ac_dim = ac_dim + + assert isinstance(obs_shapes, OrderedDict) + self.obs_shapes = obs_shapes + + # set up different observation groups for @RNN_MIMO_MLP + observation_group_shapes = OrderedDict() + observation_group_shapes["obs"] = OrderedDict(self.obs_shapes) + + self._is_goal_conditioned = False + if goal_shapes is not None and len(goal_shapes) > 0: + assert isinstance(goal_shapes, OrderedDict) + self._is_goal_conditioned = True + self.goal_shapes = OrderedDict(goal_shapes) + observation_group_shapes["goal"] = OrderedDict(self.goal_shapes) + else: + self.goal_shapes = OrderedDict() + + output_shapes = self._get_output_shapes() + super(RNNActorNetwork, self).__init__( + input_obs_group_shapes=observation_group_shapes, + output_shapes=output_shapes, + mlp_layer_dims=mlp_layer_dims, + mlp_activation=nn.ReLU, + mlp_layer_func=nn.Linear, + rnn_hidden_dim=rnn_hidden_dim, + rnn_num_layers=rnn_num_layers, + rnn_type=rnn_type, + rnn_kwargs=rnn_kwargs, + per_step=True, + encoder_kwargs=encoder_kwargs, + ) + + def _get_output_shapes(self): + """ + Allow subclasses to re-define outputs from @RNN_MIMO_MLP, since we won't + always directly predict actions, but may instead predict the parameters + of a action distribution. + """ + return OrderedDict(action=(self.ac_dim,)) + + def output_shape(self, input_shape): + # note: @input_shape should be dictionary (key: mod) + # infers temporal dimension from input shape + mod = list(self.obs_shapes.keys())[0] + T = input_shape[mod][0] + TensorUtils.assert_size_at_dim(input_shape, size=T, dim=0, + msg="RNNActorNetwork: input_shape inconsistent in temporal dimension") + return [T, self.ac_dim] + + def forward(self, obs_dict, goal_dict=None, rnn_init_state=None, return_state=False): + """ + Forward a sequence of inputs through the RNN and the per-step network. + + Args: + obs_dict (dict): batch of observations - each tensor in the dictionary + should have leading dimensions batch and time [B, T, ...] + goal_dict (dict): if not None, batch of goal observations + rnn_init_state: rnn hidden state, initialize to zero state if set to None + return_state (bool): whether to return hidden state + + Returns: + actions (torch.Tensor): predicted action sequence + rnn_state: return rnn state at the end if return_state is set to True + """ + if self._is_goal_conditioned: + assert goal_dict is not None + # repeat the goal observation in time to match dimension with obs_dict + mod = list(obs_dict.keys())[0] + goal_dict = TensorUtils.unsqueeze_expand_at(goal_dict, size=obs_dict[mod].shape[1], dim=1) + + outputs = super(RNNActorNetwork, self).forward( + obs=obs_dict, goal=goal_dict, rnn_init_state=rnn_init_state, return_state=return_state) + + if return_state: + actions, state = outputs + else: + actions = outputs + state = None + + # apply tanh squashing to ensure actions are in [-1, 1] + actions = torch.tanh(actions["action"]) + + if return_state: + return actions, state + else: + return actions + + def forward_step(self, obs_dict, goal_dict=None, rnn_state=None): + """ + Unroll RNN over single timestep to get actions. + + Args: + obs_dict (dict): batch of observations. Should not contain + time dimension. + goal_dict (dict): if not None, batch of goal observations + rnn_state: rnn hidden state, initialize to zero state if set to None + + Returns: + actions (torch.Tensor): batch of actions - does not contain time dimension + state: updated rnn state + """ + obs_dict = TensorUtils.to_sequence(obs_dict) + action, state = self.forward( + obs_dict, goal_dict, rnn_init_state=rnn_state, return_state=True) + return action[:, 0], state + + def _to_string(self): + """Info to pretty print.""" + return "action_dim={}".format(self.ac_dim) + + +class RNNGMMActorNetwork(RNNActorNetwork): + """ + An RNN GMM policy network that predicts sequences of action distributions from observation sequences. + """ + def __init__( + self, + obs_shapes, + ac_dim, + mlp_layer_dims, + rnn_hidden_dim, + rnn_num_layers, + rnn_type="LSTM", # [LSTM, GRU] + rnn_kwargs=None, + num_modes=5, + min_std=0.01, + std_activation="softplus", + low_noise_eval=True, + use_tanh=False, + goal_shapes=None, + encoder_kwargs=None, + ): + """ + Args: + + rnn_hidden_dim (int): RNN hidden dimension + + rnn_num_layers (int): number of RNN layers + + rnn_type (str): [LSTM, GRU] + + rnn_kwargs (dict): kwargs for the torch.nn.LSTM / GRU + + num_modes (int): number of GMM modes + + min_std (float): minimum std output from network + + std_activation (None or str): type of activation to use for std deviation. Options are: + + `'softplus'`: Softplus activation applied + + `'exp'`: Exp applied; this corresponds to network output being interpreted as log_std instead of std + + low_noise_eval (float): if True, model will sample from GMM with low std, so that + one of the GMM modes will be sampled (approximately) + + use_tanh (bool): if True, use a tanh-Gaussian distribution + + encoder_kwargs (dict or None): If None, results in default encoder_kwargs being applied. Otherwise, should + be nested dictionary containing relevant per-modality information for encoder networks. + Should be of form: + + obs_modality1: dict + feature_dimension: int + core_class: str + core_kwargs: dict + ... + ... + obs_randomizer_class: str + obs_randomizer_kwargs: dict + ... + ... + obs_modality2: dict + ... + """ + + # parameters specific to GMM actor + self.num_modes = num_modes + self.min_std = min_std + self.low_noise_eval = low_noise_eval + self.use_tanh = use_tanh + + # Define activations to use + self.activations = { + "softplus": F.softplus, + "exp": torch.exp, + } + assert std_activation in self.activations, \ + "std_activation must be one of: {}; instead got: {}".format(self.activations.keys(), std_activation) + self.std_activation = std_activation + + super(RNNGMMActorNetwork, self).__init__( + obs_shapes=obs_shapes, + ac_dim=ac_dim, + mlp_layer_dims=mlp_layer_dims, + rnn_hidden_dim=rnn_hidden_dim, + rnn_num_layers=rnn_num_layers, + rnn_type=rnn_type, + rnn_kwargs=rnn_kwargs, + goal_shapes=goal_shapes, + encoder_kwargs=encoder_kwargs, + ) + + def _get_output_shapes(self): + """ + Tells @MIMO_MLP superclass about the output dictionary that should be generated + at the last layer. Network outputs parameters of GMM distribution. + """ + return OrderedDict( + mean=(self.num_modes, self.ac_dim), + scale=(self.num_modes, self.ac_dim), + logits=(self.num_modes,), + ) + + def forward_train(self, obs_dict, goal_dict=None, rnn_init_state=None, return_state=False): + """ + Return full GMM distribution, which is useful for computing + quantities necessary at train-time, like log-likelihood, KL + divergence, etc. + + Args: + obs_dict (dict): batch of observations + goal_dict (dict): if not None, batch of goal observations + rnn_init_state: rnn hidden state, initialize to zero state if set to None + return_state (bool): whether to return hidden state + + Returns: + dists (Distribution): sequence of GMM distributions over the timesteps + rnn_state: return rnn state at the end if return_state is set to True + """ + if self._is_goal_conditioned: + assert goal_dict is not None + # repeat the goal observation in time to match dimension with obs_dict + mod = list(obs_dict.keys())[0] + goal_dict = TensorUtils.unsqueeze_expand_at(goal_dict, size=obs_dict[mod].shape[1], dim=1) + + outputs = RNN_MIMO_MLP.forward( + self, obs=obs_dict, goal=goal_dict, rnn_init_state=rnn_init_state, return_state=return_state) + + if return_state: + outputs, state = outputs + else: + state = None + + means = outputs["mean"] + scales = outputs["scale"] + logits = outputs["logits"] + + # apply tanh squashing to mean if not using tanh-GMM to ensure means are in [-1, 1] + if not self.use_tanh: + means = torch.tanh(means) + + if self.low_noise_eval and (not self.training): + # low-noise for all Gaussian dists + scales = torch.ones_like(means) * 1e-4 + else: + # post-process the scale accordingly + scales = self.activations[self.std_activation](scales) + self.min_std + + # mixture components - make sure that `batch_shape` for the distribution is equal + # to (batch_size, timesteps, num_modes) since MixtureSameFamily expects this shape + component_distribution = D.Normal(loc=means, scale=scales) + component_distribution = D.Independent(component_distribution, 1) # shift action dim to event shape + + # unnormalized logits to categorical distribution for mixing the modes + mixture_distribution = D.Categorical(logits=logits) + + dists = D.MixtureSameFamily( + mixture_distribution=mixture_distribution, + component_distribution=component_distribution, + ) + + if self.use_tanh: + # Wrap distribution with Tanh + dists = TanhWrappedDistribution(base_dist=dists, scale=1.) + + if return_state: + return dists, state + else: + return dists + + def forward(self, obs_dict, goal_dict=None, rnn_init_state=None, return_state=False): + """ + Samples actions from the policy distribution. + + Args: + obs_dict (dict): batch of observations + goal_dict (dict): if not None, batch of goal observations + + Returns: + action (torch.Tensor): batch of actions from policy distribution + """ + out = self.forward_train(obs_dict=obs_dict, goal_dict=goal_dict, rnn_init_state=rnn_init_state, return_state=return_state) + if return_state: + ad, state = out + return ad.sample(), state + return out.sample() + + def forward_train_step(self, obs_dict, goal_dict=None, rnn_state=None): + """ + Unroll RNN over single timestep to get action GMM distribution, which + is useful for computing quantities necessary at train-time, like + log-likelihood, KL divergence, etc. + + Args: + obs_dict (dict): batch of observations. Should not contain + time dimension. + goal_dict (dict): if not None, batch of goal observations + rnn_state: rnn hidden state, initialize to zero state if set to None + + Returns: + ad (Distribution): GMM action distributions + state: updated rnn state + """ + obs_dict = TensorUtils.to_sequence(obs_dict) + ad, state = self.forward_train( + obs_dict, goal_dict, rnn_init_state=rnn_state, return_state=True) + + # to squeeze time dimension, make another action distribution + assert ad.component_distribution.base_dist.loc.shape[1] == 1 + assert ad.component_distribution.base_dist.scale.shape[1] == 1 + assert ad.mixture_distribution.logits.shape[1] == 1 + component_distribution = D.Normal( + loc=ad.component_distribution.base_dist.loc.squeeze(1), + scale=ad.component_distribution.base_dist.scale.squeeze(1), + ) + component_distribution = D.Independent(component_distribution, 1) + mixture_distribution = D.Categorical(logits=ad.mixture_distribution.logits.squeeze(1)) + ad = D.MixtureSameFamily( + mixture_distribution=mixture_distribution, + component_distribution=component_distribution, + ) + return ad, state + + def forward_step(self, obs_dict, goal_dict=None, rnn_state=None): + """ + Unroll RNN over single timestep to get sampled actions. + + Args: + obs_dict (dict): batch of observations. Should not contain + time dimension. + goal_dict (dict): if not None, batch of goal observations + rnn_state: rnn hidden state, initialize to zero state if set to None + + Returns: + acts (torch.Tensor): batch of actions - does not contain time dimension + state: updated rnn state + """ + obs_dict = TensorUtils.to_sequence(obs_dict) + acts, state = self.forward( + obs_dict, goal_dict, rnn_init_state=rnn_state, return_state=True) + assert acts.shape[1] == 1 + return acts[:, 0], state + + def _to_string(self): + """Info to pretty print.""" + msg = "action_dim={}, std_activation={}, low_noise_eval={}, num_nodes={}, min_std={}".format( + self.ac_dim, self.std_activation, self.low_noise_eval, self.num_modes, self.min_std) + return msg + + +class TransformerActorNetwork(MIMO_Transformer): + """ + An Transformer policy network that predicts actions from observation sequences (assumed to be frame stacked + from previous observations) and possible from previous actions as well (in an autoregressive manner). + """ + def __init__( + self, + obs_shapes, + ac_dim, + transformer_embed_dim, + transformer_num_layers, + transformer_num_heads, + transformer_context_length, + transformer_emb_dropout=0.1, + transformer_attn_dropout=0.1, + transformer_block_output_dropout=0.1, + transformer_sinusoidal_embedding=False, + transformer_activation="gelu", + transformer_nn_parameter_for_timesteps=False, + goal_shapes=None, + encoder_kwargs=None, + ): + """ + Args: + + obs_shapes (OrderedDict): a dictionary that maps modality to + expected shapes for observations. + + ac_dim (int): dimension of action space. + + transformer_embed_dim (int): dimension for embeddings used by transformer + + transformer_num_layers (int): number of transformer blocks to stack + + transformer_num_heads (int): number of attention heads for each + transformer block - must divide @transformer_embed_dim evenly. Self-attention is + computed over this many partitions of the embedding dimension separately. + + transformer_context_length (int): expected length of input sequences + + transformer_embedding_dropout (float): dropout probability for embedding inputs in transformer + + transformer_attn_dropout (float): dropout probability for attention outputs for each transformer block + + transformer_block_output_dropout (float): dropout probability for final outputs for each transformer block + + goal_shapes (OrderedDict): a dictionary that maps modality to + expected shapes for goal observations. + + encoder_kwargs (dict or None): If None, results in default encoder_kwargs being applied. Otherwise, should + be nested dictionary containing relevant per-modality information for encoder networks. + Should be of form: + + obs_modality1: dict + feature_dimension: int + core_class: str + core_kwargs: dict + ... + ... + obs_randomizer_class: str + obs_randomizer_kwargs: dict + ... + ... + obs_modality2: dict + ... + """ + self.ac_dim = ac_dim + + assert isinstance(obs_shapes, OrderedDict) + self.obs_shapes = obs_shapes + + self.transformer_nn_parameter_for_timesteps = transformer_nn_parameter_for_timesteps + + # set up different observation groups for @RNN_MIMO_MLP + observation_group_shapes = OrderedDict() + observation_group_shapes["obs"] = OrderedDict(self.obs_shapes) + + self._is_goal_conditioned = False + if goal_shapes is not None and len(goal_shapes) > 0: + assert isinstance(goal_shapes, OrderedDict) + self._is_goal_conditioned = True + self.goal_shapes = OrderedDict(goal_shapes) + observation_group_shapes["goal"] = OrderedDict(self.goal_shapes) + else: + self.goal_shapes = OrderedDict() + + output_shapes = self._get_output_shapes() + super(TransformerActorNetwork, self).__init__( + input_obs_group_shapes=observation_group_shapes, + output_shapes=output_shapes, + transformer_embed_dim=transformer_embed_dim, + transformer_num_layers=transformer_num_layers, + transformer_num_heads=transformer_num_heads, + transformer_context_length=transformer_context_length, + transformer_emb_dropout=transformer_emb_dropout, + transformer_attn_dropout=transformer_attn_dropout, + transformer_block_output_dropout=transformer_block_output_dropout, + transformer_sinusoidal_embedding=transformer_sinusoidal_embedding, + transformer_activation=transformer_activation, + transformer_nn_parameter_for_timesteps=transformer_nn_parameter_for_timesteps, + + encoder_kwargs=encoder_kwargs, + ) + + def _get_output_shapes(self): + """ + Allow subclasses to re-define outputs from @MIMO_Transformer, since we won't + always directly predict actions, but may instead predict the parameters + of a action distribution. + """ + output_shapes = OrderedDict(action=(self.ac_dim,)) + return output_shapes + + def output_shape(self, input_shape): + # note: @input_shape should be dictionary (key: mod) + # infers temporal dimension from input shape + mod = list(self.obs_shapes.keys())[0] + T = input_shape[mod][0] + TensorUtils.assert_size_at_dim(input_shape, size=T, dim=0, + msg="TransformerActorNetwork: input_shape inconsistent in temporal dimension") + return [T, self.ac_dim] + + def forward(self, obs_dict, actions=None, goal_dict=None): + """ + Forward a sequence of inputs through the Transformer. + Args: + obs_dict (dict): batch of observations - each tensor in the dictionary + should have leading dimensions batch and time [B, T, ...] + actions (torch.Tensor): batch of actions of shape [B, T, D] + goal_dict (dict): if not None, batch of goal observations + Returns: + outputs (torch.Tensor or dict): contains predicted action sequence, or dictionary + with predicted action sequence and predicted observation sequences + """ + if self._is_goal_conditioned: + assert goal_dict is not None + # repeat the goal observation in time to match dimension with obs_dict + mod = list(obs_dict.keys())[0] + goal_dict = TensorUtils.unsqueeze_expand_at(goal_dict, size=obs_dict[mod].shape[1], dim=1) + + forward_kwargs = dict(obs=obs_dict, goal=goal_dict) + outputs = super(TransformerActorNetwork, self).forward(**forward_kwargs) + + # apply tanh squashing to ensure actions are in [-1, 1] + outputs["action"] = torch.tanh(outputs["action"]) + + return outputs["action"] # only action sequences + + def _to_string(self): + """Info to pretty print.""" + return "action_dim={}".format(self.ac_dim) + + +class TransformerGMMActorNetwork(TransformerActorNetwork): + """ + A Transformer GMM policy network that predicts sequences of action distributions from observation + sequences (assumed to be frame stacked from previous observations). + """ + def __init__( + self, + obs_shapes, + ac_dim, + transformer_embed_dim, + transformer_num_layers, + transformer_num_heads, + transformer_context_length, + transformer_emb_dropout=0.1, + transformer_attn_dropout=0.1, + transformer_block_output_dropout=0.1, + transformer_sinusoidal_embedding=False, + transformer_activation="gelu", + transformer_nn_parameter_for_timesteps=False, + num_modes=5, + min_std=0.01, + std_activation="softplus", + low_noise_eval=True, + use_tanh=False, + goal_shapes=None, + encoder_kwargs=None, + ): + """ + Args: + + obs_shapes (OrderedDict): a dictionary that maps modality to + expected shapes for observations. + + ac_dim (int): dimension of action space. + + transformer_embed_dim (int): dimension for embeddings used by transformer + + transformer_num_layers (int): number of transformer blocks to stack + + transformer_num_heads (int): number of attention heads for each + transformer block - must divide @transformer_embed_dim evenly. Self-attention is + computed over this many partitions of the embedding dimension separately. + + transformer_context_length (int): expected length of input sequences + + transformer_embedding_dropout (float): dropout probability for embedding inputs in transformer + + transformer_attn_dropout (float): dropout probability for attention outputs for each transformer block + + transformer_block_output_dropout (float): dropout probability for final outputs for each transformer block + + num_modes (int): number of GMM modes + + min_std (float): minimum std output from network + + std_activation (None or str): type of activation to use for std deviation. Options are: + + `'softplus'`: Softplus activation applied + + `'exp'`: Exp applied; this corresponds to network output being interpreted as log_std instead of std + + low_noise_eval (float): if True, model will sample from GMM with low std, so that + one of the GMM modes will be sampled (approximately) + + use_tanh (bool): if True, use a tanh-Gaussian distribution + + encoder_kwargs (dict or None): If None, results in default encoder_kwargs being applied. Otherwise, should + be nested dictionary containing relevant per-modality information for encoder networks. + Should be of form: + + obs_modality1: dict + feature_dimension: int + core_class: str + core_kwargs: dict + ... + ... + obs_randomizer_class: str + obs_randomizer_kwargs: dict + ... + ... + obs_modality2: dict + ... + """ + + # parameters specific to GMM actor + self.num_modes = num_modes + self.min_std = min_std + self.low_noise_eval = low_noise_eval + self.use_tanh = use_tanh + + # Define activations to use + self.activations = { + "softplus": F.softplus, + "exp": torch.exp, + } + assert std_activation in self.activations, \ + "std_activation must be one of: {}; instead got: {}".format(self.activations.keys(), std_activation) + self.std_activation = std_activation + + super(TransformerGMMActorNetwork, self).__init__( + obs_shapes=obs_shapes, + ac_dim=ac_dim, + transformer_embed_dim=transformer_embed_dim, + transformer_num_layers=transformer_num_layers, + transformer_num_heads=transformer_num_heads, + transformer_context_length=transformer_context_length, + transformer_emb_dropout=transformer_emb_dropout, + transformer_attn_dropout=transformer_attn_dropout, + transformer_block_output_dropout=transformer_block_output_dropout, + transformer_sinusoidal_embedding=transformer_sinusoidal_embedding, + transformer_activation=transformer_activation, + transformer_nn_parameter_for_timesteps=transformer_nn_parameter_for_timesteps, + encoder_kwargs=encoder_kwargs, + goal_shapes=goal_shapes, + ) + + def _get_output_shapes(self): + """ + Tells @MIMO_Transformer superclass about the output dictionary that should be generated + at the last layer. Network outputs parameters of GMM distribution. + """ + return OrderedDict( + mean=(self.num_modes, self.ac_dim), + scale=(self.num_modes, self.ac_dim), + logits=(self.num_modes,), + ) + + def forward_train(self, obs_dict, actions=None, goal_dict=None, low_noise_eval=None): + """ + Return full GMM distribution, which is useful for computing + quantities necessary at train-time, like log-likelihood, KL + divergence, etc. + Args: + obs_dict (dict): batch of observations + actions (torch.Tensor): batch of actions + goal_dict (dict): if not None, batch of goal observations + Returns: + dists (Distribution): sequence of GMM distributions over the timesteps + """ + if self._is_goal_conditioned: + assert goal_dict is not None + # repeat the goal observation in time to match dimension with obs_dict + mod = list(obs_dict.keys())[0] + goal_dict = TensorUtils.unsqueeze_expand_at(goal_dict, size=obs_dict[mod].shape[1], dim=1) + + forward_kwargs = dict(obs=obs_dict, goal=goal_dict) + + outputs = MIMO_Transformer.forward(self, **forward_kwargs) + + means = outputs["mean"] + scales = outputs["scale"] + logits = outputs["logits"] + + # apply tanh squashing to mean if not using tanh-GMM to ensure means are in [-1, 1] + if not self.use_tanh: + means = torch.tanh(means) + + if low_noise_eval is None: + low_noise_eval = self.low_noise_eval + if low_noise_eval and (not self.training): + # low-noise for all Gaussian dists + scales = torch.ones_like(means) * 1e-4 + else: + # post-process the scale accordingly + scales = self.activations[self.std_activation](scales) + self.min_std + + # mixture components - make sure that `batch_shape` for the distribution is equal + # to (batch_size, timesteps, num_modes) since MixtureSameFamily expects this shape + component_distribution = D.Normal(loc=means, scale=scales) + component_distribution = D.Independent(component_distribution, 1) # shift action dim to event shape + + # unnormalized logits to categorical distribution for mixing the modes + mixture_distribution = D.Categorical(logits=logits) + + dists = D.MixtureSameFamily( + mixture_distribution=mixture_distribution, + component_distribution=component_distribution, + ) + + if self.use_tanh: + # Wrap distribution with Tanh + dists = TanhWrappedDistribution(base_dist=dists, scale=1.) + + return dists + + def forward(self, obs_dict, actions=None, goal_dict=None): + """ + Samples actions from the policy distribution. + Args: + obs_dict (dict): batch of observations + actions (torch.Tensor): batch of actions + goal_dict (dict): if not None, batch of goal observations + Returns: + action (torch.Tensor): batch of actions from policy distribution + """ + out = self.forward_train(obs_dict=obs_dict, actions=actions, goal_dict=goal_dict) + return out.sample() + + def _to_string(self): + """Info to pretty print.""" + msg = "action_dim={}, std_activation={}, low_noise_eval={}, num_nodes={}, min_std={}".format( + self.ac_dim, self.std_activation, self.low_noise_eval, self.num_modes, self.min_std) + return msg + + +class VAEActor(Module): + """ + A VAE that models a distribution of actions conditioned on observations. + The VAE prior and decoder are used at test-time as the policy. + """ + def __init__( + self, + obs_shapes, + ac_dim, + encoder_layer_dims, + decoder_layer_dims, + latent_dim, + device, + decoder_is_conditioned=True, + decoder_reconstruction_sum_across_elements=False, + latent_clip=None, + prior_learn=False, + prior_is_conditioned=False, + prior_layer_dims=(), + prior_use_gmm=False, + prior_gmm_num_modes=10, + prior_gmm_learn_weights=False, + prior_use_categorical=False, + prior_categorical_dim=10, + prior_categorical_gumbel_softmax_hard=False, + goal_shapes=None, + encoder_kwargs=None, + ): + """ + Args: + obs_shapes (OrderedDict): a dictionary that maps modality to + expected shapes for observations. + + ac_dim (int): dimension of action space. + + goal_shapes (OrderedDict): a dictionary that maps modality to + expected shapes for goal observations. + + encoder_kwargs (dict or None): If None, results in default encoder_kwargs being applied. Otherwise, should + be nested dictionary containing relevant per-modality information for encoder networks. + Should be of form: + + obs_modality1: dict + feature_dimension: int + core_class: str + core_kwargs: dict + ... + ... + obs_randomizer_class: str + obs_randomizer_kwargs: dict + ... + ... + obs_modality2: dict + ... + """ + super(VAEActor, self).__init__() + + self.obs_shapes = obs_shapes + self.ac_dim = ac_dim + action_shapes = OrderedDict(action=(self.ac_dim,)) + + # ensure VAE decoder will squash actions into [-1, 1] + output_squash = ['action'] + output_scales = OrderedDict(action=1.) + + self._vae = VAE( + input_shapes=action_shapes, + output_shapes=action_shapes, + encoder_layer_dims=encoder_layer_dims, + decoder_layer_dims=decoder_layer_dims, + latent_dim=latent_dim, + device=device, + condition_shapes=self.obs_shapes, + decoder_is_conditioned=decoder_is_conditioned, + decoder_reconstruction_sum_across_elements=decoder_reconstruction_sum_across_elements, + latent_clip=latent_clip, + output_squash=output_squash, + output_scales=output_scales, + prior_learn=prior_learn, + prior_is_conditioned=prior_is_conditioned, + prior_layer_dims=prior_layer_dims, + prior_use_gmm=prior_use_gmm, + prior_gmm_num_modes=prior_gmm_num_modes, + prior_gmm_learn_weights=prior_gmm_learn_weights, + prior_use_categorical=prior_use_categorical, + prior_categorical_dim=prior_categorical_dim, + prior_categorical_gumbel_softmax_hard=prior_categorical_gumbel_softmax_hard, + goal_shapes=goal_shapes, + encoder_kwargs=encoder_kwargs, + ) + + def encode(self, actions, obs_dict, goal_dict=None): + """ + Args: + actions (torch.Tensor): a batch of actions + + obs_dict (dict): a dictionary that maps modalities to torch.Tensor + batches. These should correspond to the observation modalities + used for conditioning in either the decoder or the prior (or both). + + goal_dict (dict): a dictionary that maps modalities to torch.Tensor + batches. These should correspond to goal modalities. + + Returns: + posterior params (dict): dictionary with the following keys: + + mean (torch.Tensor): posterior encoder means + + logvar (torch.Tensor): posterior encoder logvars + """ + inputs = OrderedDict(action=actions) + return self._vae.encode(inputs=inputs, conditions=obs_dict, goals=goal_dict) + + def decode(self, obs_dict=None, goal_dict=None, z=None, n=None): + """ + Thin wrapper around @VaeNets.VAE implementation. + + Args: + obs_dict (dict): a dictionary that maps modalities to torch.Tensor + batches. Only needs to be provided if @decoder_is_conditioned + or @z is None (since the prior will require it to generate z). + + goal_dict (dict): a dictionary that maps modalities to torch.Tensor + batches. These should correspond to goal modalities. + + z (torch.Tensor): if provided, these latents are used to generate + reconstructions from the VAE, and the prior is not sampled. + + n (int): this argument is used to specify the number of samples to + generate from the prior. Only required if @z is None - i.e. + sampling takes place + + Returns: + recons (dict): dictionary of reconstructed inputs (this will be a dictionary + with a single "action" key) + """ + return self._vae.decode(conditions=obs_dict, goals=goal_dict, z=z, n=n) + + def sample_prior(self, obs_dict=None, goal_dict=None, n=None): + """ + Thin wrapper around @VaeNets.VAE implementation. + + Args: + n (int): this argument is used to specify the number + of samples to generate from the prior. + + obs_dict (dict): a dictionary that maps modalities to torch.Tensor + batches. Only needs to be provided if @prior_is_conditioned. + + goal_dict (dict): a dictionary that maps modalities to torch.Tensor + batches. These should correspond to goal modalities. + + Returns: + z (torch.Tensor): latents sampled from the prior + """ + return self._vae.sample_prior(n=n, conditions=obs_dict, goals=goal_dict) + + def set_gumbel_temperature(self, temperature): + """ + Used by external algorithms to schedule Gumbel-Softmax temperature, + which is used during reparametrization at train-time. Should only be + used if @prior_use_categorical is True. + """ + self._vae.set_gumbel_temperature(temperature) + + def get_gumbel_temperature(self): + """ + Return current Gumbel-Softmax temperature. Should only be used if + @prior_use_categorical is True. + """ + return self._vae.get_gumbel_temperature() + + def output_shape(self, input_shape=None): + """ + This implementation is required by the Module superclass, but is unused since we + never chain this module to other ones. + """ + return [self.ac_dim] + + def forward_train(self, actions, obs_dict, goal_dict=None, freeze_encoder=False): + """ + A full pass through the VAE network used during training to construct KL + and reconstruction losses. See @VAE class for more info. + + Args: + actions (torch.Tensor): a batch of actions + + obs_dict (dict): a dictionary that maps modalities to torch.Tensor + batches. These should correspond to the observation modalities + used for conditioning in either the decoder or the prior (or both). + + goal_dict (dict): a dictionary that maps modalities to torch.Tensor + batches. These should correspond to goal modalities. + + Returns: + vae_outputs (dict): a dictionary that contains the following outputs. + + encoder_params (dict): parameters for the posterior distribution + from the encoder forward pass + + encoder_z (torch.Tensor): latents sampled from the encoder posterior + + decoder_outputs (dict): action reconstructions from the decoder + + kl_loss (torch.Tensor): KL loss over the batch of data + + reconstruction_loss (torch.Tensor): reconstruction loss over the batch of data + """ + action_inputs = OrderedDict(action=actions) + return self._vae.forward( + inputs=action_inputs, + outputs=action_inputs, + conditions=obs_dict, + goals=goal_dict, + freeze_encoder=freeze_encoder) + + def forward(self, obs_dict, goal_dict=None, z=None): + """ + Samples actions from the policy distribution. + + Args: + obs_dict (dict): batch of observations + goal_dict (dict): if not None, batch of goal observations + z (torch.Tensor): if not None, use the provided batch of latents instead + of sampling from the prior + + Returns: + action (torch.Tensor): batch of actions from policy distribution + """ + n = None + if z is None: + # prior will be sampled - so we must provide number of samples explicitly + mod = list(obs_dict.keys())[0] + n = obs_dict[mod].shape[0] + return self.decode(obs_dict=obs_dict, goal_dict=goal_dict, z=z, n=n)["action"] diff --git a/docs/training_runs/bc_rnn_ensemble_pick_place_new_start/Dev-IK-Rel-v0/model0/bc_new_demos/20250924112632/config.json b/docs/training_runs/Dev-IK-Rel-v0/model0/bc_new_demos/20250925163039/config.json similarity index 97% rename from docs/training_runs/bc_rnn_ensemble_pick_place_new_start/Dev-IK-Rel-v0/model0/bc_new_demos/20250924112632/config.json rename to docs/training_runs/Dev-IK-Rel-v0/model0/bc_new_demos/20250925163039/config.json index a6b76e34fba4..6ec2c3912eed 100644 --- a/docs/training_runs/bc_rnn_ensemble_pick_place_new_start/Dev-IK-Rel-v0/model0/bc_new_demos/20250924112632/config.json +++ b/docs/training_runs/Dev-IK-Rel-v0/model0/bc_new_demos/20250925163039/config.json @@ -36,8 +36,8 @@ } }, "train": { - "data": "docs/beaker_lift_new_start_skillgen_generated_split_normalized.hdf5", - "output_dir": "/workspace/isaaclab/logs/docs/training_runs/bc_rnn_ensemble_pick_place_new_start/Dev-IK-Rel-v0/model14/", + "data": "docs/beaker_lift_new_start_skillgen_generated_split.hdf5", + "output_dir": "/docs/robomimic/Dev-IK-Rel-v0/model14/", "num_data_workers": 1, "hdf5_cache_mode": "low_dim", "hdf5_use_swmr": true, diff --git a/docs/training_runs/bc_rnn_ensemble_pick_place_new_start/Dev-IK-Rel-v0/model0/bc_new_demos/20250924123612/config.json b/docs/training_runs/Dev-IK-Rel-v0/model1/bc_new_demos/20250925163039/config.json similarity index 97% rename from docs/training_runs/bc_rnn_ensemble_pick_place_new_start/Dev-IK-Rel-v0/model0/bc_new_demos/20250924123612/config.json rename to docs/training_runs/Dev-IK-Rel-v0/model1/bc_new_demos/20250925163039/config.json index a6b76e34fba4..6ec2c3912eed 100644 --- a/docs/training_runs/bc_rnn_ensemble_pick_place_new_start/Dev-IK-Rel-v0/model0/bc_new_demos/20250924123612/config.json +++ b/docs/training_runs/Dev-IK-Rel-v0/model1/bc_new_demos/20250925163039/config.json @@ -36,8 +36,8 @@ } }, "train": { - "data": "docs/beaker_lift_new_start_skillgen_generated_split_normalized.hdf5", - "output_dir": "/workspace/isaaclab/logs/docs/training_runs/bc_rnn_ensemble_pick_place_new_start/Dev-IK-Rel-v0/model14/", + "data": "docs/beaker_lift_new_start_skillgen_generated_split.hdf5", + "output_dir": "/docs/robomimic/Dev-IK-Rel-v0/model14/", "num_data_workers": 1, "hdf5_cache_mode": "low_dim", "hdf5_use_swmr": true, diff --git a/docs/training_runs/bc_trans/bc_trans/20250721085238/config.json b/docs/training_runs/Dev-IK-Rel-v0/model2/bc_new_demos/20250925163039/config.json old mode 100755 new mode 100644 similarity index 94% rename from docs/training_runs/bc_trans/bc_trans/20250721085238/config.json rename to docs/training_runs/Dev-IK-Rel-v0/model2/bc_new_demos/20250925163039/config.json index cc08f0289374..6ec2c3912eed --- a/docs/training_runs/bc_trans/bc_trans/20250721085238/config.json +++ b/docs/training_runs/Dev-IK-Rel-v0/model2/bc_new_demos/20250925163039/config.json @@ -1,18 +1,18 @@ { "algo_name": "bc", "experiment": { - "name": "bc_trans_20250721_1", + "name": "bc_new_demos", "validate": true, "logging": { "terminal_output_to_txt": true, "log_tb": true, "log_wandb": true, - "wandb_proj_name": "pick-place-tabletop" + "wandb_proj_name": "Lift_beaker_new_demos" }, "save": { "enabled": true, "every_n_seconds": null, - "every_n_epochs": 100, + "every_n_epochs": null, "epochs": [], "on_best_validation": true, "on_best_rollout_return": true, @@ -20,7 +20,7 @@ }, "epoch_every_n_steps": 100, "validation_epoch_every_n_steps": 10, - "env": null, + "env": "Dev-IK-Rel-v0", "additional_envs": null, "render": false, "render_video": true, @@ -36,8 +36,8 @@ } }, "train": { - "data": "docs/varying_beaker_scale_generated2_split.hdf5", - "output_dir": "/docs/training_runs/bc_trans/202507210951/Dev-IK-Rel-v0", + "data": "docs/beaker_lift_new_start_skillgen_generated_split.hdf5", + "output_dir": "/docs/robomimic/Dev-IK-Rel-v0/model14/", "num_data_workers": 1, "hdf5_cache_mode": "low_dim", "hdf5_use_swmr": true, @@ -57,7 +57,7 @@ "goal_mode": null, "cuda": true, "batch_size": 100, - "num_epochs": 3000, + "num_epochs": 2000, "seed": 101 }, "algo": { @@ -132,7 +132,7 @@ ] }, "rnn": { - "enabled": false, + "enabled": true, "horizon": 10, "hidden_dim": 400, "rnn_type": "LSTM", @@ -143,7 +143,7 @@ } }, "transformer": { - "enabled": true, + "enabled": false, "context_length": 10, "embed_dim": 512, "num_layers": 6, diff --git a/docs/training_runs/bc_trans/bc_trans/20250721074750/config.json b/docs/training_runs/Dev-IK-Rel-v0/model3/bc_new_demos/20250925163039/config.json old mode 100755 new mode 100644 similarity index 94% rename from docs/training_runs/bc_trans/bc_trans/20250721074750/config.json rename to docs/training_runs/Dev-IK-Rel-v0/model3/bc_new_demos/20250925163039/config.json index 90570d60c2f4..6ec2c3912eed --- a/docs/training_runs/bc_trans/bc_trans/20250721074750/config.json +++ b/docs/training_runs/Dev-IK-Rel-v0/model3/bc_new_demos/20250925163039/config.json @@ -1,18 +1,18 @@ { "algo_name": "bc", "experiment": { - "name": "bc_trans_20250721_1", + "name": "bc_new_demos", "validate": true, "logging": { "terminal_output_to_txt": true, "log_tb": true, "log_wandb": true, - "wandb_proj_name": "pick-place-tabletop" + "wandb_proj_name": "Lift_beaker_new_demos" }, "save": { "enabled": true, "every_n_seconds": null, - "every_n_epochs": 100, + "every_n_epochs": null, "epochs": [], "on_best_validation": true, "on_best_rollout_return": true, @@ -20,7 +20,7 @@ }, "epoch_every_n_steps": 100, "validation_epoch_every_n_steps": 10, - "env": null, + "env": "Dev-IK-Rel-v0", "additional_envs": null, "render": false, "render_video": true, @@ -36,8 +36,8 @@ } }, "train": { - "data": "docs/varying_beaker_scale_generated2_split.hdf5", - "output_dir": "/docs/docs/training_runs/bc_trans/202507210830/Dev-IK-Rel-v0", + "data": "docs/beaker_lift_new_start_skillgen_generated_split.hdf5", + "output_dir": "/docs/robomimic/Dev-IK-Rel-v0/model14/", "num_data_workers": 1, "hdf5_cache_mode": "low_dim", "hdf5_use_swmr": true, @@ -57,7 +57,7 @@ "goal_mode": null, "cuda": true, "batch_size": 100, - "num_epochs": 3000, + "num_epochs": 2000, "seed": 101 }, "algo": { @@ -132,7 +132,7 @@ ] }, "rnn": { - "enabled": false, + "enabled": true, "horizon": 10, "hidden_dim": 400, "rnn_type": "LSTM", @@ -143,7 +143,7 @@ } }, "transformer": { - "enabled": true, + "enabled": false, "context_length": 10, "embed_dim": 512, "num_layers": 6, diff --git a/docs/training_runs/Dev-IK-Rel-v0/model4/bc_new_demos/20250925163039/config.json b/docs/training_runs/Dev-IK-Rel-v0/model4/bc_new_demos/20250925163039/config.json new file mode 100644 index 000000000000..6ec2c3912eed --- /dev/null +++ b/docs/training_runs/Dev-IK-Rel-v0/model4/bc_new_demos/20250925163039/config.json @@ -0,0 +1,216 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_new_demos", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "Lift_beaker_new_demos" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": null, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": true, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": "Dev-IK-Rel-v0", + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": "docs/beaker_lift_new_start_skillgen_generated_split.hdf5", + "output_dir": "/docs/robomimic/Dev-IK-Rel-v0/model14/", + "num_data_workers": 1, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": "train", + "hdf5_validation_filter_key": "valid", + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.01, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gaussian": { + "enabled": false, + "fixed_std": false, + "init_std": 0.1, + "min_std": 0.01, + "std_activation": "softplus", + "low_noise_eval": true + }, + "gmm": { + "enabled": true, + "num_modes": 10, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object_position", + "target_object_position", + "joint_pos", + "joint_vel", + "actions" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + }, + "meta": { + "hp_base_config_file": null, + "hp_keys": [], + "hp_values": [] + } +} \ No newline at end of file diff --git a/docs/training_runs/Dev-IK-Rel-v1/model0/bc_high_data_/20251009101852/config.json b/docs/training_runs/Dev-IK-Rel-v1/model0/bc_high_data_/20251009101852/config.json new file mode 100644 index 000000000000..f3cc5a6a7da1 --- /dev/null +++ b/docs/training_runs/Dev-IK-Rel-v1/model0/bc_high_data_/20251009101852/config.json @@ -0,0 +1,219 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_high_data_", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "Lift_beaker_new_demos_high_qual" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": null, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": true, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": "Dev-IK-Rel-v1", + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": "docs/beaker_lift_high_demo_generated_split_normalized.hdf5", + "output_dir": "/workspace/isaaclab/logs/robomimic/Dev-IK-Rel-v1/model9/", + "num_data_workers": 4, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": "train", + "hdf5_validation_filter_key": "valid", + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.01, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [ + 1024, + 1024 + ], + "gaussian": { + "enabled": false, + "fixed_std": false, + "init_std": 0.1, + "min_std": 0.01, + "std_activation": "softplus", + "low_noise_eval": true + }, + "gmm": { + "enabled": true, + "num_modes": 10, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object_position", + "target_object_position", + "joint_pos", + "joint_vel", + "actions" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + }, + "meta": { + "hp_base_config_file": null, + "hp_keys": [], + "hp_values": [] + } +} \ No newline at end of file diff --git a/docs/training_runs/Dev-IK-Rel-v1/model1/bc_high_data_/20251009101852/config.json b/docs/training_runs/Dev-IK-Rel-v1/model1/bc_high_data_/20251009101852/config.json new file mode 100644 index 000000000000..f3cc5a6a7da1 --- /dev/null +++ b/docs/training_runs/Dev-IK-Rel-v1/model1/bc_high_data_/20251009101852/config.json @@ -0,0 +1,219 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_high_data_", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "Lift_beaker_new_demos_high_qual" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": null, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": true, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": "Dev-IK-Rel-v1", + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + 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"meta": { + "hp_base_config_file": null, + "hp_keys": [], + "hp_values": [] + } +} \ No newline at end of file diff --git a/docs/training_runs/good_data/bc_high_demos_4/20251008124639/config.json b/docs/training_runs/good_data/bc_high_demos_4/20251008124639/config.json new file mode 100644 index 000000000000..3887d5ed63a6 --- /dev/null +++ b/docs/training_runs/good_data/bc_high_demos_4/20251008124639/config.json @@ -0,0 +1,219 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_high_demos_4", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "Lift_beaker_new_demos_high_qual" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": null, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": true, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": "Dev-IK-Rel-v1", + "additional_envs": 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"epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [ + 1024, + 1024 + ], + "gaussian": { + "enabled": false, + "fixed_std": false, + "init_std": 0.1, + "min_std": 0.01, + "std_activation": "softplus", + "low_noise_eval": true + }, + "gmm": { + "enabled": true, + "num_modes": 10, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object_position", + "target_object_position", + "joint_pos", + "joint_vel", + "actions" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + }, + "meta": { + "hp_base_config_file": null, + "hp_keys": [], + "hp_values": [] + } +} \ No newline at end of file diff --git a/docs/training_runs/good_data/bc_high_demos_5/20251008131706/config.json b/docs/training_runs/good_data/bc_high_demos_5/20251008131706/config.json new file mode 100644 index 000000000000..9adcb6d9c51d --- /dev/null +++ b/docs/training_runs/good_data/bc_high_demos_5/20251008131706/config.json @@ -0,0 +1,219 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_high_demos_5", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "Lift_beaker_new_demos_high_qual" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": null, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": true, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": "Dev-IK-Rel-v1", + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": "docs/beaker_lift_high_demo_generated_split.hdf5", + "output_dir": "/workspace/isaaclab/logs/robomimic/Dev-IK-Rel-v1", + "num_data_workers": 4, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": "train", + "hdf5_validation_filter_key": "valid", + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.01, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [ + 1024, + 1024 + ], + "gaussian": { + "enabled": false, + "fixed_std": false, + "init_std": 0.1, + "min_std": 0.01, + "std_activation": "softplus", + "low_noise_eval": true + }, + "gmm": { + "enabled": true, + "num_modes": 10, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object_position", + "target_object_position", + "joint_pos", + "joint_vel", + "actions" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + }, + "meta": { + "hp_base_config_file": null, + "hp_keys": [], + "hp_values": [] + } +} \ No newline at end of file diff --git a/docs/training_runs/good_data/bc_low_demos/20251007171556/config.json b/docs/training_runs/good_data/bc_low_demos/20251007171556/config.json new file mode 100644 index 000000000000..cbeeec17626a --- /dev/null +++ b/docs/training_runs/good_data/bc_low_demos/20251007171556/config.json @@ -0,0 +1,219 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_low_demos", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "Lift_beaker_new_demos_low_qual" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": null, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": true, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": "Dev-IK-Rel-v1", + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": "docs/beaker_lift_high_demo_generated_split.hdf5", + "output_dir": "/workspace/isaaclab/logs/robomimic/Dev-IK-Rel-v1", + "num_data_workers": 4, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": "train", + "hdf5_validation_filter_key": "valid", + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.01, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [ + 1024, + 1024 + ], + "gaussian": { + "enabled": false, + "fixed_std": false, + "init_std": 0.1, + "min_std": 0.01, + "std_activation": "softplus", + "low_noise_eval": true + }, + "gmm": { + "enabled": true, + "num_modes": 10, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object_position", + "target_object_position", + "joint_pos", + "joint_vel", + "actions" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + }, + "meta": { + "hp_base_config_file": null, + "hp_keys": [], + "hp_values": [] + } +} \ No newline at end of file diff --git a/scripts/imitation_learning/robomimic/play_ensemble.py b/scripts/imitation_learning/robomimic/play_ensemble.py index 01b5d1f65f1d..2286052e3f11 100644 --- a/scripts/imitation_learning/robomimic/play_ensemble.py +++ b/scripts/imitation_learning/robomimic/play_ensemble.py @@ -313,39 +313,7 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, u if bool(success_term.func(env, **success_term.params)[0]): return True, traj, recovery_activated_during_rollout - # while rec_i < recovery_duration: - # # print("In recovery") - # recovery.append(True) - # robot.set_joint_position_target(safe_pos) # expects an absolute joint position - # env.scene.write_data_to_sim() - # env.sim.step() - - # # Check if robot reached the safe position within a small tolerance - # new_joint_positions = get_joint_pos(env) - # target_joint_positions = safe_pos - - # position_error = torch.abs(new_joint_positions - target_joint_positions) - # position_errors.append(position_error) - # tolerance = 1e-2 - - # # check if the positon/position error has converged to the safe position (it wont reach it exactly). - # # it also jumps to it straight away but if you stop straight away you get weird jerky motion and it doesnt actually reach the safe position - # # check every 50 iterations - # if rec_i % 50 == 0: - # reached_safe_pos_early = False - # if position_errors.maxlen == len(position_errors): - # for pe in position_errors: - # if torch.all(torch.abs(pe - position_error) < tolerance): - # reached_safe_pos_early = True - # if reached_safe_pos_early: - # print(f"Reached safe position early.") - # break - # # else extend the recovery duration - # if rec_i == recovery_duration - 1: - # print("Extending Recovery Duration") - # recovery_duration += 50 - - # rec_i += 1 + print(f"Recovery Mode Ended") print("Recovery Cooldown Activated") @@ -691,7 +659,8 @@ def main(): # load the stack cube ensemble # stack_cube_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/stack_cube_ensemble.txt') - pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/ensembles.txt') + #pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/ensembles.txt') + pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/high_qual_ensembles.txt') # pick_place_ensemble_30 = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/pick_place_ensemble_30_paths.txt') diff --git a/scripts/imitation_learning/robomimic/train_ensemble.py b/scripts/imitation_learning/robomimic/train_ensemble.py index 67f6ba3160fc..744753d669b1 100644 --- a/scripts/imitation_learning/robomimic/train_ensemble.py +++ b/scripts/imitation_learning/robomimic/train_ensemble.py @@ -263,13 +263,13 @@ def train(config: Config, device: str, log_dirs: list[str], ckpt_dirs: list[str] #config.algo.transformer.num_heads = 1 for model_num in range(ensemble_size): # setup a wand run - wand_experiment_name = f"{config.experiment.name}-model{model_num}" + wand_experiment_name = f"{config.experiment.name}-model_{model_num}" run=wandb.init( project=config.experiment.logging.wandb_proj_name, name=wand_experiment_name ) - #run.define_metric("train reward", step_metric="episode") - # run.define_metric("episode error", step_metric="episode") + # run.define_metric("train reward", step_metric="episode") + # run.define_metric("episode error", step_metric="episode") # reset best valid loss best_valid_loss = None @@ -278,7 +278,7 @@ def train(config: Config, device: str, log_dirs: list[str], ckpt_dirs: list[str] video_dir = video_dirs[model_num] # setup logger stuff - # logger = Logger(os.path.join(log_dir, "log.txt")) + #logger = Logger(os.path.join(log_dir, "log.txt")) # sys.stdout = logger # sys.stderr = logger data_logger = DataLogger(log_dir, config=config, log_tb=config.experiment.logging.log_tb, log_wandb=config.experiment.logging.log_wandb) @@ -286,6 +286,7 @@ def train(config: Config, device: str, log_dirs: list[str], ckpt_dirs: list[str] json.dump(config, outfile, indent=4) # initialise current network in the ensemble + model = algo_factory( algo_name=config.algo_name, config=config, @@ -371,7 +372,7 @@ def train(config: Config, device: str, log_dirs: list[str], ckpt_dirs: list[str] mem_usage = int(process.memory_info().rss / 1000000) data_logger.record("System/RAM Usage (MB)", mem_usage, epoch) print(f"\nEpoch {epoch} Memory Usage: {mem_usage} MB\n") - + run.finish() #config.algo.transformer.num_layers = config.algo.transformer.num_layers + 1 #config.algo.transformer.num_heads = config.algo.transformer.num_heads + 1 # run.finish() @@ -439,16 +440,16 @@ def main(args: argparse.Namespace): for stream in (sys.stdout, sys.stderr): if not hasattr(stream, "isatty"): stream.isatty = lambda: False - os.environ["WANDB_MODE"] = "offline" - run=wandb.init( - project=config.experiment.logging.wandb_proj_name, - name=config.experiment.name - ) + #os.environ["WANDB_MODE"] = "offline" + # run=wandb.init( + # project=config.experiment.logging.wandb_proj_name, + # name=config.experiment.name + # ) # run.define_metric("train reward", step_metric="episode") # run.define_metric("episode error", step_metric="episode") # change location of experiment directory - config.train.output_dir = os.path.abspath(os.path.join("./logs", args.log_dir, args.task)) + config.train.output_dir = os.path.abspath(os.path.join("", args.log_dir, args.task)) original_output_dir = config.train.output_dir log_dirs, ckpt_dirs, video_dirs = [], [], [] @@ -490,7 +491,7 @@ def main(args: argparse.Namespace): except Exception as e: res_str = f"run failed with error:\n{e}\n\n{traceback.format_exc()}" print(res_str) - run.finish() + # run.finish() if __name__ == "__main__": diff --git a/scripts/tools/copy_metadata_hdf5.py b/scripts/tools/copy_metadata_hdf5.py new file mode 100644 index 000000000000..85fbcc84bf1b --- /dev/null +++ b/scripts/tools/copy_metadata_hdf5.py @@ -0,0 +1,32 @@ +import h5py +import json + +# File and group +HDF5_FILE = "docs/subset_first5.hdf5" +DATA_GROUP = "data" + +# Metadata to add +metadata = { + "env_name": "Cube-Mimic-v0", + "type": 2, + "sim_args": { + "dt": 0.01, + "decimation": 2, + "render_interval": 2, + "num_envs": 1 + } +} + +# Open file in append mode +with h5py.File(HDF5_FILE, "a") as f: + data_group = f[DATA_GROUP] + + # Store each top-level key as an attribute + for key, value in metadata.items(): + if isinstance(value, dict): + # Convert nested dicts to JSON string + data_group.attrs[key] = json.dumps(value) + else: + data_group.attrs[key] = value + +print(f"✅ Metadata added to /{DATA_GROUP}") diff --git a/scripts/tools/hdf5_splitter.py b/scripts/tools/hdf5_splitter.py new file mode 100644 index 000000000000..a16e9d07e1fe --- /dev/null +++ b/scripts/tools/hdf5_splitter.py @@ -0,0 +1,60 @@ +import h5py +import numpy as np + +# ----------------------------- +# CONFIG +# ----------------------------- +INPUT_FILE = "docs/beaker_lift_new_start_skillgen_annotated.hdf5" +OUTPUT_FILE = "docs/subset_first5_clean.hdf5" +DATA_GROUP = "data" +DEMO_PREFIX = "demo_" +NUM_DEMOS = 5 + +# Metadata groups to copy (common in IsaacLab) +METADATA_GROUPS = ["meta", "env", "info", "config", "episodes", "stats"] + +# ----------------------------- +# STEP 1: Create subset HDF5 +# ----------------------------- +with h5py.File(INPUT_FILE, "r") as src, h5py.File(OUTPUT_FILE, "w") as dst: + # 1. Copy root attributes + for k, v in src.attrs.items(): + dst.attrs[k] = v + + # 2. Copy metadata groups + for g in METADATA_GROUPS: + if g in src: + src.copy(src[g], dst, name=g) + print(f"✅ Copied metadata group: /{g}") + + # 3. Copy /data group and first N demos + src_data = src[DATA_GROUP] + dst_data = dst.create_group(DATA_GROUP) + + demo_keys = sorted([k for k in src_data.keys() if k.startswith(DEMO_PREFIX)])[:NUM_DEMOS] + + for key in demo_keys: + src.copy(src_data[key], dst_data, name=key) + print(f"✅ Copied demo: /{DATA_GROUP}/{key}") + + # ----------------------------- + # STEP 2: Trim metadata arrays to match only the first N demos + # ----------------------------- + # Example metadata: episodes/episode_lengths, episodes/episode_starts, etc. + if "episodes" in dst: + episodes_group = dst["episodes"] + for name, dataset in episodes_group.items(): + if isinstance(dataset, h5py.Dataset): + # Trim first NUM_DEMOS entries + new_data = dataset[0:NUM_DEMOS] + del episodes_group[name] + episodes_group.create_dataset(name, data=new_data) + print(f"✅ Trimmed episodes/{name} to first {NUM_DEMOS} entries") + + # Some attributes may reference total episodes/envs + for attr_key in ["num_episodes", "num_envs"]: + if attr_key in dst.attrs: + dst.attrs[attr_key] = NUM_DEMOS + print(f"✅ Updated root attribute: {attr_key}={NUM_DEMOS}") + +print(f"\n🎉 Subset with {NUM_DEMOS} demos created successfully: {OUTPUT_FILE}") diff --git a/source/isaaclab/isaaclab/envs/mdp/events.py b/source/isaaclab/isaaclab/envs/mdp/events.py index c0c3afd618bf..e0739f12e182 100644 --- a/source/isaaclab/isaaclab/envs/mdp/events.py +++ b/source/isaaclab/isaaclab/envs/mdp/events.py @@ -1045,6 +1045,62 @@ def reset_root_state_uniform( asset.write_root_pose_to_sim(torch.cat([positions, orientations], dim=-1), env_ids=env_ids) asset.write_root_velocity_to_sim(velocities, env_ids=env_ids) +def reset_multiple_root_state_uniform( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + pose_range: dict[str, tuple[float, float]], + velocity_range: dict[str, tuple[float, float]], + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + asset2_cfg: SceneEntityCfg=SceneEntityCfg("stirplate") +): + """Reset the asset root state to a random position and velocity uniformly within the given ranges. + + This function randomizes the root position and velocity of the asset. + + * It samples the root position from the given ranges and adds them to the default root position, before setting + them into the physics simulation. + * It samples the root orientation from the given ranges and sets them into the physics simulation. + * It samples the root velocity from the given ranges and sets them into the physics simulation. + + The function takes a dictionary of pose and velocity ranges for each axis and rotation. The keys of the + dictionary are ``x``, ``y``, ``z``, ``roll``, ``pitch``, and ``yaw``. The values are tuples of the form + ``(min, max)``. If the dictionary does not contain a key, the position or velocity is set to zero for that axis. + """ + # extract the used quantities (to enable type-hinting) + asset: RigidObject | Articulation = env.scene[asset_cfg.name] + asset2: RigidObject | Articulation = env.scene[asset2_cfg.name] + # get default root state + root_states = asset.data.default_root_state[env_ids].clone() + root_states2 = asset2.data.default_root_state[env_ids].clone() + # poses + range_list = [pose_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z", "roll", "pitch", "yaw"]] + ranges = torch.tensor(range_list, device=asset.device) + #print(f"ranges : {ranges}") + rand_samples = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (len(env_ids), 6), device=asset.device) + + positions = root_states[:, 0:3] + env.scene.env_origins[env_ids] + rand_samples[:, 0:3] + #print(f"positions : ", positions) + + orientations_delta = math_utils.quat_from_euler_xyz(rand_samples[:, 3], rand_samples[:, 4], rand_samples[:, 5]) + orientations = math_utils.quat_mul(root_states[:, 3:7], orientations_delta) + orientations2_delta = math_utils.quat_from_euler_xyz(rand_samples[:, 3], rand_samples[:, 4], rand_samples[:, 5]) + orientations2 = math_utils.quat_mul(root_states2[:, 3:7], orientations_delta) + + # velocities + range_list = [velocity_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z", "roll", "pitch", "yaw"]] + ranges = torch.tensor(range_list, device=asset.device) + rand_samples = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (len(env_ids), 6), device=asset.device) + + velocities = root_states[:, 7:13] + rand_samples + positions[:,2] += 0.06 + # set into the physics simulation + asset.write_root_pose_to_sim(torch.cat([positions, orientations], dim=-1), env_ids=env_ids) + asset.write_root_velocity_to_sim(velocities, env_ids=env_ids) + # remote iks plate offset + positions[:,0] += 0.04 + positions[:,2] -= 0.06 + asset2.write_root_pose_to_sim(torch.cat([positions, orientations2], dim=-1), env_ids=env_ids) + asset2.write_root_velocity_to_sim(velocities, env_ids=env_ids) def reset_root_state_with_random_orientation( env: ManagerBasedEnv, @@ -1519,12 +1575,7 @@ class randomize_visual_color(ManagerTermBase): """ def __init__(self, cfg: EventTermCfg, env: ManagerBasedEnv): - """Initialize the randomization term. - - Args: - cfg: The configuration of the event term. - env: The environment instance. - """ + """Initialize the randomization term.""" super().__init__(cfg, env) # enable replicator extension if not already enabled @@ -1534,6 +1585,8 @@ def __init__(self, cfg: EventTermCfg, env: ManagerBasedEnv): # read parameters from the configuration asset_cfg: SceneEntityCfg = cfg.params.get("asset_cfg") + colors = cfg.params.get("colors") + event_name = cfg.params.get("event_name") mesh_name: str = cfg.params.get("mesh_name", "") # type: ignore # check to make sure replicate_physics is set to False, else raise error @@ -1555,51 +1608,27 @@ def __init__(self, cfg: EventTermCfg, env: ManagerBasedEnv): mesh_prim_path = f"{asset.cfg.prim_path}{mesh_name}" # TODO: Need to make it work for multiple meshes. - # extract the replicator version - version = re.match(r"^(\d+\.\d+\.\d+)", rep.__file__.split("/")[-5][21:]).group(1) - - # use different path for different version of replicator - if compare_versions(version, "1.12.4") < 0: - colors = cfg.params.get("colors") - event_name = cfg.params.get("event_name") - - # parse the colors into replicator format - if isinstance(colors, dict): - # (r, g, b) - low, high --> (low_r, low_g, low_b) and (high_r, high_g, high_b) - color_low = [colors[key][0] for key in ["r", "g", "b"]] - color_high = [colors[key][1] for key in ["r", "g", "b"]] - colors = rep.distribution.uniform(color_low, color_high) - else: - colors = list(colors) - - # Create the omni-graph node for the randomization term - def rep_color_randomization(): - prims_group = rep.get.prims(path_pattern=mesh_prim_path) - with prims_group: - rep.randomizer.color(colors=colors) - - return prims_group.node - - # Register the event to the replicator - with rep.trigger.on_custom_event(event_name=event_name): - rep_color_randomization() + # parse the colors into replicator format + if isinstance(colors, dict): + # (r, g, b) - low, high --> (low_r, low_g, low_b) and (high_r, high_g, high_b) + color_low = [colors[key][0] for key in ["r", "g", "b"]] + color_high = [colors[key][1] for key in ["r", "g", "b"]] + colors = rep.distribution.uniform(color_low, color_high) else: - stage = get_current_stage() - prims_group = rep.functional.get.prims(path_pattern=mesh_prim_path, stage=stage) + colors = list(colors) - num_prims = len(prims_group) - self.color_rng = rep.rng.ReplicatorRNG() + # Create the omni-graph node for the randomization term + def rep_texture_randomization(): + prims_group = rep.get.prims(path_pattern=mesh_prim_path) - # Create the material first and bind it to the prims - for i, prim in enumerate(prims_group): - # Disable instancble - if prim.IsInstanceable(): - prim.SetInstanceable(False) + with prims_group: + rep.randomizer.color(colors=colors) - # TODO: Should we specify the value when creating the material? - self.material_prims = rep.functional.create_batch.material( - mdl="OmniPBR.mdl", bind_prims=prims_group, count=num_prims, project_uvw=True - ) + return prims_group.node + + # Register the event to the replicator + with rep.trigger.on_custom_event(event_name=event_name): + rep_texture_randomization() def __call__( self, @@ -1610,34 +1639,11 @@ def __call__( colors: list[tuple[float, float, float]] | dict[str, tuple[float, float]], mesh_name: str = "", ): - # note: This triggers the nodes for all the environments. - # We need to investigate how to make it happen only for a subset based on env_ids. - - # we import the module here since we may not always need the replicator + # import replicator import omni.replicator.core as rep - version = re.match(r"^(\d+\.\d+\.\d+)", rep.__file__.split("/")[-5][21:]).group(1) - - # use different path for different version of replicator - if compare_versions(version, "1.12.4") < 0: - rep.utils.send_og_event(event_name) - else: - colors = colors if colors else self._cfg.params.get("colors") - - # parse the colors into replicator format - if isinstance(colors, dict): - # (r, g, b) - low, high --> (low_r, low_g, low_b) and (high_r, high_g, high_b) - color_low = [colors[key][0] for key in ["r", "g", "b"]] - color_high = [colors[key][1] for key in ["r", "g", "b"]] - colors = [color_low, color_high] - else: - colors = list(colors) - - num_prims = len(self.material_prims) - random_colors = self.color_rng.generator.uniform(colors[0], colors[1], size=(num_prims, 3)) - - rep.functional.modify.attribute(self.material_prims, "diffuse_color_constant", random_colors) - + # only send the event to the replicator + rep.utils.send_og_event(event_name) """ Internal helper functions. diff --git a/source/isaaclab/isaaclab/utils/datasets/hdf5_dataset_file_handler.py b/source/isaaclab/isaaclab/utils/datasets/hdf5_dataset_file_handler.py index 2fa35ca1533a..13f07dddf378 100644 --- a/source/isaaclab/isaaclab/utils/datasets/hdf5_dataset_file_handler.py +++ b/source/isaaclab/isaaclab/utils/datasets/hdf5_dataset_file_handler.py @@ -81,9 +81,13 @@ def set_env_name(self, env_name: str): def get_env_name(self) -> str | None: """Get the environment name.""" self._raise_if_not_initialized() - env_args = json.loads(self._hdf5_data_group.attrs["env_args"]) - if "env_name" in env_args: - return env_args["env_name"] + try: + env_args = json.loads(self._hdf5_data_group.attrs["env_args"]) + if "env_name" in env_args: + return env_args["env_name"] + + except: + return "Cube-Mimic-v0" return None def get_episode_names(self) -> Iterable[str]: diff --git a/source/isaaclab_assets/isaaclab_assets/__init__.py b/source/isaaclab_assets/isaaclab_assets/__init__.py index 5b4a782caffa..4c0a0182484b 100644 --- a/source/isaaclab_assets/isaaclab_assets/__init__.py +++ b/source/isaaclab_assets/isaaclab_assets/__init__.py @@ -22,3 +22,4 @@ from .robots import * from .sensors import * +from .glassware import * \ No newline at end of file diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_blueprint_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_blueprint_mimic_env_cfg.py index 698abcab7c7a..b73ed476a072 100755 --- a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_blueprint_mimic_env_cfg.py +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_blueprint_mimic_env_cfg.py @@ -6,7 +6,7 @@ from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig from isaaclab.utils import configclass -from isaaclab_tasks.manager_based.manipulation.cube_lift.config.franka.dev_ik_rel_env_cfg import FrankaDevEnvCfg +from isaaclab_tasks.manager_based.manipulation.cube_lift.config.franka.dev_ik_rel_env_obs import FrankaDevEnvCfg @configclass diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py index 982b1320bac6..cf0eb52fdc4f 100755 --- a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py @@ -6,7 +6,7 @@ from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig from isaaclab.utils import configclass -from isaaclab_tasks.manager_based.manipulation.cube_lift.config.franka.dev_ik_rel_env_cfg import FrankaDevEnvCfg +from isaaclab_tasks.manager_based.manipulation.cube_lift.config.franka.dev_ik_rel_env_obs import FrankaDevEnvCfg @configclass diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py index f290d45c4b1a..4059bfcf8045 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py @@ -130,11 +130,12 @@ entry_point="isaaclab.envs:ManagerBasedRLEnv", kwargs={ "env_cfg_entry_point": f"{__name__}.dev_ik_rel_env_obs:FrankaDevEnvCfg", - "robomimic_bc_rnn_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), + + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), "robomimic_bc_trans_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_trans.json"), "robomimic_hbc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/hbc.json"), "robomimic_bcq_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bcq.json"), - "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc.json"), + }, disable_env_checker=True, ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json index 6b09227a280f..db8907778f4b 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json @@ -1,13 +1,13 @@ { "algo_name": "bc", "experiment": { - "name": "bc_new_demos", + "name": "bc_med_data_", "validate": true, "logging": { "terminal_output_to_txt": true, "log_tb": true, "log_wandb": true, - "wandb_proj_name": "Lift_beaker_new_demos" + "wandb_proj_name": "Lift_beaker_new_demos_med_qual" }, "save": { "enabled": true, @@ -20,7 +20,7 @@ }, "epoch_every_n_steps": 100, "validation_epoch_every_n_steps": 10, - "env": "Dev-IK-Rel-v0", + "env": "Dev-IK-Rel-v1", "additional_envs": null, "render": false, "render_video": true, @@ -36,8 +36,8 @@ }, "train": { "data": null, - "output_dir": "/training_runs/bc_rnn", - "num_data_workers": 1, + "output_dir": "docs/training_runs/bc_rnn", + "num_data_workers": 4, "hdf5_cache_mode": "low_dim", "hdf5_use_swmr": true, "hdf5_normalize_obs": false, @@ -76,7 +76,7 @@ "l1_weight": 0.0, "cos_weight": 0.0 }, - "actor_layer_dims": [], + "gmm": { "enabled": true, "num_modes": 10, @@ -84,9 +84,7 @@ "std_activation": "softplus", "low_noise_eval": true }, - "vae": { - "enabled": false - }, + "rnn": { "enabled": true, "horizon": 10, @@ -97,21 +95,8 @@ "kwargs": { "bidirectional": false } - }, - "transformer": { - "enabled": false, - "context_length": 10, - "embed_dim": 512, - "num_layers": 6, - "num_heads": 8, - "emb_dropout": 0.1, - "attn_dropout": 0.1, - "block_output_dropout": 0.1, - "sinusoidal_embedding": false, - "activation": "gelu", - "supervise_all_steps": false, - "nn_parameter_for_timesteps": true } + }, "observation": { "modalities": { diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_temp.json similarity index 100% rename from source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc.json rename to source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_temp.json diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_env_cfg.py index bdf7eb7a81cc..896391c9b6dd 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_env_cfg.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_env_cfg.py @@ -14,7 +14,7 @@ from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg from isaaclab_tasks.manager_based.manipulation.cube_lift import mdp from isaaclab_tasks.manager_based.manipulation.cube_lift.lift_env_cfg import CubeEnvCfg -#from isaaclab_assets.glassware.glassware_objects import Chem_Assets +from isaaclab_assets.glassware.glassware import ChemistryGlassware import math @@ -71,9 +71,13 @@ def __post_init__(self): self.commands.object_pose.body_name = "panda_hand" self.commands.object_pose.body_name = "panda_hand" - # glassware = Chem_Assets() + glassware = ChemistryGlassware() # Set each stacking cube deterministically - #self.scene.object = glassware.beaker(pos=[0.5, 0.0, 0.01],name="Object") + self.scene.object = glassware.beaker(pos=[0.5, 0.0, 0.02], rot=[0, 0, 1, 0],name="Object") + #self.scene.object = glassware.flask(pos=[0.5, 0.0, 0.03], rot=[0, 0, 1, 0],name="Object", scale=(0.7,0.7,0.7)) + #self.scene.stirplate = glassware.stirplate(pos=[0.5, 0.0, 0.01]) + #self.scene.object = glassware.vial(pos=[0.5, 0.0, 0.02], rot=[0, 0, 1, 0],name="Object") + #### everything else leave default # self.scene.flask = glassware.flask() # self.scene.vial = glassware.vial() @@ -83,22 +87,22 @@ def __post_init__(self): # self.scene.obstacle = glassware.cube_obs(pos= [0.8, -0.8, 0.0],rot=[0, 0, 1, 0], name="cube_obs") #self.scene.obstacle = glassware.stirplate(pos= [0.5, -0.32, 0.0],rot=[0.707, 0, 0, -0.707], name="cube_obs") # self.scene.obstacle = glassware.stirplate(pos= [0.8, -0.8, 0.0], rot=[0.707, 0, 0, -0.707], name="cube_obs") - self.scene.object = RigidObjectCfg( - prim_path="{ENV_REGEX_NS}/Object", - init_state=RigidObjectCfg.InitialStateCfg( pos=[0.5, 0.0, 0.02],rot=[0, 0, 1, 0]), - spawn=UsdFileCfg( - usd_path=f"source/isaaclab_assets/isaaclab_assets/glassware/beaker/beaker.usd", - scale=(0.5, 0.5, 0.5), - rigid_props=RigidBodyPropertiesCfg( - solver_position_iteration_count=16, - solver_velocity_iteration_count=1, - max_angular_velocity=1000.0, - max_linear_velocity=1000.0, - max_depenetration_velocity=5.0, - disable_gravity=False, - ), - ), - ) + # self.scene.object = RigidObjectCfg( + # prim_path="{ENV_REGEX_NS}/Object", + # init_state=RigidObjectCfg.InitialStateCfg( pos=[0.5, 0.0, 0.02],rot=[0, 0, 1, 0]), + # spawn=UsdFileCfg( + # usd_path=f"source/isaaclab_assets/isaaclab_assets/glassware/beaker/beaker.usd", + # scale=(0.5, 0.5, 0.5), + # rigid_props=RigidBodyPropertiesCfg( + # solver_position_iteration_count=16, + # solver_velocity_iteration_count=1, + # max_angular_velocity=1000.0, + # max_linear_velocity=1000.0, + # max_depenetration_velocity=5.0, + # disable_gravity=False, + # ), + # ), + # ) # Listens to the required transforms marker_cfg = FRAME_MARKER_CFG.copy() marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_obs.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_obs.py index 4332e690b196..9404c5d07aae 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_obs.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_obs.py @@ -6,22 +6,55 @@ from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg from isaaclab.utils import configclass -from isaaclab.assets import RigidObjectCfg - +from isaaclab.assets import RigidObjectCfg, ArticulationCfg +from isaaclab.managers import EventTermCfg as EventTerm from . import dev_env_cfg - +import math +from isaaclab.managers import SceneEntityCfg +from isaaclab_assets.glassware.glassware import ChemistryGlassware +from isaaclab_tasks.manager_based.manipulation.cube_lift import mdp ## # Pre-defined configs ## from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip -#from isaaclab_assets.glassware.glassware_objects import Chem_Assets + + @configclass class FrankaDevEnvCfg(dev_env_cfg.FrankaDevEnvCfg): def __post_init__(self): # post init of parent super().__post_init__() - + # put the beaker on the stir plate + glassware = ChemistryGlassware() + self.scene.stirplate = glassware.stirplate(pos=[0.5, 0.0, 0.01]) + self.events.reset_object_position = EventTerm( + func=mdp.reset_multiple_root_state_uniform, + mode="reset", + params={ + "pose_range": {"x": (0, 0.2), "y": (0, 0.25), "z": (0.02, 0.02)}, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("object", body_names="Object"), + "asset2_cfg" : SceneEntityCfg("stirplate") + }, + ) + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace( + prim_path="{ENV_REGEX_NS}/Robot", + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + + "panda_joint1": 0.3281, + "panda_joint2": -0.3684, + "panda_joint3": -0.2787, + "panda_joint4": -2.6138, + "panda_joint5": -2.7527, + "panda_joint6": 2.4991, # +90° → keeps hand level + "panda_joint7": 0.3331, + "panda_finger_joint1": 0.04, # open gripper + "panda_finger_joint2": 0.04, + } + ), + ) # replace with relative position controller self.actions.arm_action = DifferentialInverseKinematicsActionCfg( asset_name="robot", @@ -29,10 +62,9 @@ def __post_init__(self): body_name="panda_hand", controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), scale=0.5, - body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), ) - # glassware = Chem_Assets() - #self.scene.obstacle = glassware.cube_obs(pos= [0.5, -0.1, 0.0],rot=[0, 0, 1, 0], name="cube_obs") + diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py index 5fa05b6c8f4e..a2b7772d26c5 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py @@ -143,8 +143,8 @@ class PolicyCfg(ObsGroup): #robot_pose=ObsTerm(func=mdp.robot_pose) #object_to_target = ObsTerm(func=mdp.position_command_error) #norm_error = ObsTerm(func=mdp.object_goal_norm_error) - abs_joint_pos = ObsTerm(func=mdp.get_joint_pos) - object_knocked = ObsTerm(func=mdp.object_knocked) + # abs_joint_pos = ObsTerm(func=mdp.get_joint_pos) + #object_knocked = ObsTerm(func=mdp.object_knocked) def __post_init__(self): self.enable_corruption = False self.concatenate_terms = False @@ -225,7 +225,7 @@ class EventCfg: func=mdp.reset_root_state_uniform, mode="reset", params={ - "pose_range": {"x": (0, 0.2), "y": (0, 0.25), "z": (0.05, 0.1)}, + "pose_range": {"x": (0, 0.2), "y": (0, 0.25), "z": (0.02, 0.05)}, "velocity_range": {}, "asset_cfg": SceneEntityCfg("object", body_names="Object"), }, @@ -242,6 +242,10 @@ class EventCfg: "scale_range": {"x": (0.5, 0.5), "y": (0.5, 0.5), "z": (0.6, 0.7)}, "asset_cfg": SceneEntityCfg("object", body_names="Object"), }, + # params={ + # "scale_range": {"x": (1.0, 1.0), "y": (1.0, 1.0), "z": (1.0, 1.0)}, + # "asset_cfg": SceneEntityCfg("object", body_names="Object"), + # }, ) @@ -298,6 +302,7 @@ class TerminationsCfg: object_dropping = DoneTerm( func=mdp.root_height_below_minimum, params={"minimum_height": -0.05, "asset_cfg": SceneEntityCfg("object")} ) + object_tipped = DoneTerm(func=mdp.object_knocked) #fix object dropping success = DoneTerm(func=mdp.object_near_goal) From cbd95bab30916c7440fd3ff0410290a37e43c85b Mon Sep 17 00:00:00 2001 From: LJones-alt Date: Wed, 15 Oct 2025 17:37:25 +0100 Subject: [PATCH 03/15] added source docs to gitignore --- .gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 18d749f04b96..4cf7259980ca 100644 --- a/.gitignore +++ b/.gitignore @@ -71,7 +71,7 @@ _build /datasets/ /franka_robotiq/ /source/isaaclab_assets/ - +/docs/source # Tests tests/ From a804d8e91a525cda06f28fa6fcb258f7b67c0c1b Mon Sep 17 00:00:00 2001 From: LJones-alt Date: Thu, 30 Oct 2025 16:05:51 +0000 Subject: [PATCH 04/15] backup controller borked --- docker/Dockerfile.base | 4 +- docker/docker-compose.yaml | 4 +- .../bc_med_data_/20251015111922/config.json | 219 +++++++ .../bc_med_data_/20251015111922/config.json | 219 +++++++ .../bc_med_data_/20251015111922/config.json | 219 +++++++ .../bc_med_data_/20251015111922/config.json | 219 +++++++ .../bc_med_data_/20251015111922/config.json | 219 +++++++ .../bc_med_data_/20251015111922/config.json | 219 +++++++ .../bc_med_data_/20251015111922/config.json | 219 +++++++ .../bc_med_data_/20251015111922/config.json | 219 +++++++ .../bc_med_data_/20251015111922/config.json | 219 +++++++ .../bc_med_data_/20251015111922/config.json | 219 +++++++ .../bc_med_data_/20251015094239/config.json | 219 +++++++ .../bc_med_data_/20251015094240/config.json | 219 +++++++ .../bc_med_data_/20251015094240/config.json | 219 +++++++ .../bc_med_data_/20251015094240/config.json | 219 +++++++ .../bc_med_data_/20251015094241/config.json | 219 +++++++ .../robomimic/play_controller.py | 287 ++++++++ .../robomimic/play_ensemble.py | 617 +++++++++--------- .../tutorials/05_controllers/run_diff_ik.py | 23 +- .../05_controllers/run_diff_ik_rel.py | 250 +++++++ .../isaaclab/controllers/differential_ik.py | 3 + .../isaaclab/envs/mdp/terminations.py | 2 + source/isaaclab/isaaclab/safety/__init__.py | 1 + .../isaaclab/safety/switchingLogic.py | 47 ++ .../config/franka/dev_ik_rel_env_place.py | 82 +++ .../manipulation/cube_lift/lift_env_cfg.py | 6 +- .../cube_lift/mdp/observations.py | 6 +- 28 files changed, 4281 insertions(+), 336 deletions(-) create mode 100644 docs/training_runs/Dev-IK-Rel-v1/model0/bc_med_data_/20251015111922/config.json create mode 100644 docs/training_runs/Dev-IK-Rel-v1/model1/bc_med_data_/20251015111922/config.json create mode 100644 docs/training_runs/Dev-IK-Rel-v1/model2/bc_med_data_/20251015111922/config.json create mode 100644 docs/training_runs/Dev-IK-Rel-v1/model3/bc_med_data_/20251015111922/config.json create mode 100644 docs/training_runs/Dev-IK-Rel-v1/model4/bc_med_data_/20251015111922/config.json create mode 100644 docs/training_runs/Dev-IK-Rel-v1/model5/bc_med_data_/20251015111922/config.json create mode 100644 docs/training_runs/Dev-IK-Rel-v1/model6/bc_med_data_/20251015111922/config.json create mode 100644 docs/training_runs/Dev-IK-Rel-v1/model7/bc_med_data_/20251015111922/config.json create mode 100644 docs/training_runs/Dev-IK-Rel-v1/model8/bc_med_data_/20251015111922/config.json create mode 100644 docs/training_runs/Dev-IK-Rel-v1/model9/bc_med_data_/20251015111922/config.json create mode 100644 docs/training_runs/bc_rnn_ensemble_mid_data/Dev-IK-Rel-v1/model5/bc_med_data_/20251015094239/config.json create mode 100644 docs/training_runs/bc_rnn_ensemble_mid_data/Dev-IK-Rel-v1/model6/bc_med_data_/20251015094240/config.json create mode 100644 docs/training_runs/bc_rnn_ensemble_mid_data/Dev-IK-Rel-v1/model7/bc_med_data_/20251015094240/config.json create mode 100644 docs/training_runs/bc_rnn_ensemble_mid_data/Dev-IK-Rel-v1/model8/bc_med_data_/20251015094240/config.json create mode 100644 docs/training_runs/bc_rnn_ensemble_mid_data/Dev-IK-Rel-v1/model9/bc_med_data_/20251015094241/config.json create mode 100644 scripts/imitation_learning/robomimic/play_controller.py create mode 100644 scripts/tutorials/05_controllers/run_diff_ik_rel.py create mode 100644 source/isaaclab/isaaclab/safety/__init__.py create mode 100644 source/isaaclab/isaaclab/safety/switchingLogic.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py diff --git a/docker/Dockerfile.base b/docker/Dockerfile.base index 4ebf8291ca03..53f528244e25 100644 --- a/docker/Dockerfile.base +++ b/docker/Dockerfile.base @@ -120,8 +120,8 @@ ENV CUDA_HOME=/usr/local/cuda-12.8 ENV PATH=${CUDA_HOME}/bin:${PATH} ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH} ENV TORCH_CUDA_ARCH_LIST=8.0+PTX -RUN ${ISAACLAB_PATH}/isaaclab.sh -p -m pip install --no-build-isolation \ - "nvidia-curobo @ git+https://github.com/NVlabs/curobo.git@ebb71702f3f70e767f40fd8e050674af0288abe8" +# RUN ${ISAACLAB_PATH}/isaaclab.sh -p -m pip install --no-build-isolation \ +# "nvidia-curobo @ git+https://github.com/NVlabs/curobo.git@ebb71702f3f70e767f40fd8e050674af0288abe8" # aliasing isaaclab.sh and python for convenience RUN echo "export ISAACLAB_PATH=${ISAACLAB_PATH}" >> ${HOME}/.bashrc && \ echo "alias isaaclab=${ISAACLAB_PATH}/isaaclab.sh" >> ${HOME}/.bashrc && \ diff --git a/docker/docker-compose.yaml b/docker/docker-compose.yaml index cde0175a7996..f1cf86c2daa9 100644 --- a/docker/docker-compose.yaml +++ b/docker/docker-compose.yaml @@ -113,7 +113,7 @@ services: devices: - /dev/hidraw0:/dev/hidraw0 - /dev/hidraw1:/dev/hidraw1 - # - /dev/hidraw2:/dev/hidraw2 + - /dev/hidraw2:/dev/hidraw2 # This service adds a ROS2 Humble # installation on top of the base image @@ -146,7 +146,7 @@ services: devices: - /dev/hidraw0:/dev/hidraw0 - /dev/hidraw1:/dev/hidraw1 - # - /dev/hidraw2:/dev/hidraw2 + - /dev/hidraw2:/dev/hidraw2 volumes: # isaac-sim diff --git a/docs/training_runs/Dev-IK-Rel-v1/model0/bc_med_data_/20251015111922/config.json b/docs/training_runs/Dev-IK-Rel-v1/model0/bc_med_data_/20251015111922/config.json new file mode 100644 index 000000000000..619458cefb42 --- /dev/null +++ b/docs/training_runs/Dev-IK-Rel-v1/model0/bc_med_data_/20251015111922/config.json @@ -0,0 +1,219 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_med_data_", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "Lift_beaker_new_demos_med_qual" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": null, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": true, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": "Dev-IK-Rel-v1", + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": "docs/beaker_lift_subset_generated_normalized.hdf5", + "output_dir": "/workspace/isaaclab/robomimic/Dev-IK-Rel-v1/model9/", + "num_data_workers": 4, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": "train", + "hdf5_validation_filter_key": "valid", + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.01, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [ + 1024, + 1024 + ], + "gaussian": { + "enabled": false, + "fixed_std": false, + "init_std": 0.1, + "min_std": 0.01, + "std_activation": "softplus", + "low_noise_eval": true + }, + "gmm": { + "enabled": true, + "num_modes": 10, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object_position", + "target_object_position", + "joint_pos", + "joint_vel", + "actions" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "depth": { + "core_class": 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000000000000..36406eafde29 --- /dev/null +++ b/docs/training_runs/bc_rnn_ensemble_mid_data/Dev-IK-Rel-v1/model8/bc_med_data_/20251015094240/config.json @@ -0,0 +1,219 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_med_data_", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "Lift_beaker_new_demos_med_qual" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": null, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": true, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": "Dev-IK-Rel-v1", + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": 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"obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + }, + "meta": { + "hp_base_config_file": null, + "hp_keys": [], + "hp_values": [] + } +} \ No newline at end of file diff --git a/docs/training_runs/bc_rnn_ensemble_mid_data/Dev-IK-Rel-v1/model9/bc_med_data_/20251015094241/config.json b/docs/training_runs/bc_rnn_ensemble_mid_data/Dev-IK-Rel-v1/model9/bc_med_data_/20251015094241/config.json new file mode 100644 index 000000000000..36406eafde29 --- /dev/null +++ b/docs/training_runs/bc_rnn_ensemble_mid_data/Dev-IK-Rel-v1/model9/bc_med_data_/20251015094241/config.json @@ -0,0 +1,219 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_med_data_", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "Lift_beaker_new_demos_med_qual" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": null, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": true, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": "Dev-IK-Rel-v1", + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": "docs/beaker_lift_subset_generated_normalized.hdf5", + "output_dir": "/workspace/isaaclab/docs/training_runs/bc_rnn_ensemble_mid_data/Dev-IK-Rel-v1/model9/", + "num_data_workers": 4, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": "train", + "hdf5_validation_filter_key": "valid", + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.01, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [ + 1024, + 1024 + ], + "gaussian": { + "enabled": false, + "fixed_std": false, + "init_std": 0.1, + "min_std": 0.01, + "std_activation": "softplus", + "low_noise_eval": true + }, + "gmm": { + "enabled": true, + "num_modes": 10, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object_position", + "target_object_position", + "joint_pos", + "joint_vel", + "actions" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + }, + "meta": { + "hp_base_config_file": null, + "hp_keys": [], + "hp_values": [] + } +} \ No newline at end of file diff --git a/scripts/imitation_learning/robomimic/play_controller.py b/scripts/imitation_learning/robomimic/play_controller.py new file mode 100644 index 000000000000..1246c87535ab --- /dev/null +++ b/scripts/imitation_learning/robomimic/play_controller.py @@ -0,0 +1,287 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# run this script: +# ./isaaclab.sh -p scripts/imitation_learning/robomimic/play_ensemble.py --device cuda --task Dev-IK-Rel-v0 --num_rollouts 100 +# stack cube task: ./isaaclab.sh -p scripts/imitation_learning/robomimic/play.py --device cuda --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --num_rollouts 50 +"""Script to play and evaluate a trained policy from robomimic. + +This script loads a robomimic policy and plays it in an Isaac Lab environment. + +Args: + task: Name of the environment. + checkpoint: Path to the robomimic policy checkpoint. + horizon: If provided, override the step horizon of each rollout. + num_rollouts: If provided, override the number of rollouts. + seed: If provided, overeride the default random seed. + norm_factor_min: If provided, minimum value of the action space normalization factor. + norm_factor_max: If provided, maximum value of the action space normalization factor. +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Evaluate robomimic policy for Isaac Lab environment.") +parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.") +parser.add_argument("--video_length", type=int, default=800, help="Length of the recorded video (in steps).") +parser.add_argument( + "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations." +) +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument("--checkpoint", type=str, default=None, help="Pytorch model checkpoint to load.") +parser.add_argument("--horizon", type=int, default=2000, help="Step horizon of each rollout.") +parser.add_argument("--num_rollouts", type=int, default=1, help="Number of rollouts.") +parser.add_argument("--seed", type=int, default=101, help="Random seed.") +parser.add_argument( + "--norm_factor_min", type=float, default=None, help="Optional: minimum value of the normalization factor." +) +parser.add_argument( + "--norm_factor_max", type=float, default=None, help="Optional: maximum value of the normalization factor." +) +parser.add_argument("--enable_pinocchio", default=False, action="store_true", help="Enable Pinocchio.") + +parser.add_argument("--model_name", type=str, default=None) + +group = parser.add_mutually_exclusive_group() +group.add_argument("--use_recovery", type=bool ,default= False, help="Enable recovery mechanism. By default recovery is enabled.") + +parser.add_argument("--ensemble_size", type=int, default=10) + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +if args_cli.enable_pinocchio: + # Import pinocchio before AppLauncher to force the use of the version installed by IsaacLab and not the one installed by Isaac Sim + # pinocchio is required by the Pink IK controllers and the GR1T2 retargeter + import pinocchio # noqa: F401 +import numpy as np +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import time +import copy +import random +import os +import gymnasium as gym +import torch +import math +from collections import deque + +import robomimic.utils.file_utils as FileUtils +import robomimic.utils.torch_utils as TorchUtils +from isaaclab_tasks.manager_based.manipulation.cube_lift.mdp.observations import get_joint_pos, object_grasped +#from chills.tasks.mdp.observations import get_joint_pos, object_grasped +if args_cli.enable_pinocchio: + import isaaclab_tasks.manager_based.manipulation.pick_place # noqa: F401 + +from isaaclab_tasks.utils import parse_env_cfg +#from isaaclab.utils.logging_helper import LoggingHelper, ErrorType, LogType +from isaaclab.utils.math import matrix_from_quat, convert_quat, quat_mul, euler_xyz_from_quat +from evaluation import ensemble_uncertainty +from isaaclab.safety.switchingLogic import SwitchingLogic +from isaaclab.controllers import DifferentialIKController, DifferentialIKControllerCfg +from isaaclab.utils.math import subtract_frame_transforms, euler_xyz_from_quat +from isaaclab.managers import SceneEntityCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg + +#import chills.tasks +#from chills.tasks.mdp import upright_tilt_deg + +def rollout_ensemble(env, success_term, horizon, device): + """Perform a single rollout of the policy in the environment. + + Args: + policy: The robomimicpolicy to play. + env: The environment to play in. + horizon: The step horizon of each rollout. + device: The device to run the policy on. + + Returns: + terminated: Whether the rollout terminated. + traj: The trajectory of the rollout. + """ + + + obs_dict, _ = env.reset() + #print(f"obs_dict type: {type(obs_dict)},\nobs_dict contents:\n {obs_dict}") + + traj = dict(actions=[], obs=[], next_obs=[], sub_obs=[], + uncertainties=[], min_actions=[], max_actions=[], + time_taken=[], joint_pos=[], recovery=[], failure=[], grasp=[], appr=[]) + + ###### SETUP SWITCHING LOGIC #### + + sim = env.unwrapped.sim + num_envs = env.num_envs + + + certain_joint_positions = [] + + ### SET USE RECOVERY TO FALSE #### + + + # Set up recovery controller + robot = env.unwrapped.scene["robot"] + print(f"Unwrapped sim : {sim}") + ee_recovery_pos = torch.tensor([[0.5206, 0.0096, 0.3751]], device=device) + + ee_recovery_rot = torch.tensor([[ 0.6664, 0.0360, 0.7414, -0.0705]], device=device) + # lets change this into the 6 element tensor they are expecting + roll,pitch,yaw = euler_xyz_from_quat(ee_recovery_rot) + ee_recovery_goal = torch.cat([ee_recovery_pos, ee_recovery_rot], dim =-1) + print(f"I got this roll {roll}, pitch {pitch}, yaw {yaw} and full tensor {torch.cat([roll, pitch, yaw], dim=-1)}\n i need to add with {ee_recovery_pos}") + ee_rpy = torch.cat([roll, pitch, yaw], dim=-1) + ee_rpy = ee_rpy.unsqueeze(0) + ee_goal = torch.cat([ee_recovery_pos, ee_rpy], dim=1) + # Create controller + diff_ik_cfg = DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls") + diff_ik_controller = DifferentialIKController(diff_ik_cfg, num_envs=env.unwrapped.scene.num_envs, device=sim.device) + + + robot_entity_cfg = SceneEntityCfg("robot", joint_names=["panda_joint.*"], body_names=["panda_hand"]) + robot_entity_cfg.resolve(env.unwrapped.scene) + if robot.is_fixed_base: + ee_jacobi_idx = robot_entity_cfg.body_ids[0] - 1 + else: + ee_jacobi_idx = robot_entity_cfg.body_ids[0] + + print(f'Running for horizon {horizon}') + for i in range(horizon): + jacobian = robot.root_physx_view.get_jacobians()[:, ee_jacobi_idx, :, robot_entity_cfg.joint_ids] + ee_pose_w = robot.data.body_pose_w[:, robot_entity_cfg.body_ids[0]] + root_pose_w = robot.data.root_pose_w + joint_pos = robot.data.joint_pos[:, robot_entity_cfg.joint_ids] + + # current EE pose in base frame + ee_pos_b, ee_quat_b = subtract_frame_transforms( + root_pose_w[:, 0:3], root_pose_w[:, 3:7], + ee_pose_w[:, 0:3], ee_pose_w[:, 3:7] + ) + print(f"ee_pos_b {ee_pos_b}, ee_quat_b : {ee_quat_b}") + + # target EE pose (absolute, world frame → base frame) + target_pos_w = ee_recovery_pos # [1,3] + target_quat_w = ee_recovery_rot # [1,4] + target_pos_b, target_quat_b = subtract_frame_transforms( + root_pose_w[:, 0:3], root_pose_w[:, 3:7], + target_pos_w, target_quat_w + ) + print(f"target_pos_b {target_pos_b}, target_quat_b {target_quat_b}") + + # --- compute relative pose command (6D) --- + # translation delta + delta_pos = target_pos_b - ee_pos_b # [1,3] + print(f"Delta pos {delta_pos}") + # rotation delta (ΔR = R_target * R_currentᵀ) + delta_rot = target_quat_b - ee_quat_b + print(f"Delat Rot {delta_rot}") + # convert ΔR to euler angles (XYZ convention) + r,p,y = euler_xyz_from_quat(delta_rot, wrap_to_2pi=False) + print(f"[DEBUG] Delat R {r}, P {p}, Y {y}") + delta_rpy = torch.cat([r,p,y], dim=-1) + delta_rpy = torch.unsqueeze(delta_rpy,0) + print(f"[DEBUG] RPY {delta_rpy}") + # --- build relative command --- + ee_goal = torch.cat([delta_pos, delta_rpy], dim=-1) # [1,6] + + + # debug info + print(f"[DEBUG] ee_goal (Δx,Δy,Δz,Δr,Δp,Δy): {ee_goal}") + print(f"[DEBUG] Current pos_b: {ee_pos_b}") + print(f"[DEBUG] Target pos_b: {target_pos_b}") + + # --- send command to controller --- + diff_ik_controller.set_command(ee_goal, ee_pos_b, ee_quat_b) + + # compute joint targets + joint_pos_des = diff_ik_controller.compute(ee_pos_b, ee_quat_b, jacobian, joint_pos) + + # (optional) denormalize actions if necessary + if args_cli.norm_factor_min is not None and args_cli.norm_factor_max is not None: + actions = ((actions + 1) * (args_cli.norm_factor_max - args_cli.norm_factor_min)) / 2 + args_cli.norm_factor_min + + # --- execute recovery movement --- + pos_err = torch.norm(target_pos_b - ee_pos_b) + actions=joint_pos_des + metrics = ensemble_uncertainty(ensemble, obs) + metrics['min']=actions + metrics['max']=actions + uncertainty = 0 + + if pos_err > 1e-3: + robot.set_joint_position_target(joint_pos_des, joint_ids=robot_entity_cfg.joint_ids) + env.scene.write_data_to_sim() + obs_dict, _, terminated, truncated, _ = env.step(joint_pos_des) + else: + print(f"[Recovery] Target reached — ending recovery mode.") + recovery_mode = False + recovery_cooldown_active = True + print("[Recovery] Cooldown Activated.") + + + + + + + + +def main(): + """Run a trained policy from robomimic with Isaac Lab environment.""" + # parse configuration + env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=1, use_fabric=not args_cli.disable_fabric) + + # Set observations to dictionary mode for Robomimic + env_cfg.observations.policy.concatenate_terms = False + + #create a log handler + # loghelper = LoggingHelper() + + + + # Disable recorder + env_cfg.recorders = None + + # Extract success checking function + success_term = env_cfg.terminations.success + env_cfg.terminations.success = None + arm_action = env_cfg.actions.arm_action + # Get timeout signal + timeout = env_cfg.terminations.time_out + #env_cfg.terminations.time_out = None + + # Create environment + env = gym.make(args_cli.task, cfg=env_cfg).unwrapped + + # Set seed9 + torch.manual_seed(args_cli.seed) + env.seed(args_cli.seed) + + # Acquire device + device = TorchUtils.get_torch_device(try_to_use_cuda=True) + + for trial in range(args_cli.num_rollouts): + print(f"[INFO] Starting trial {trial}") + + terminated, traj, recovery_activated_during_rollout = rollout_ensemble( env, success_term, args_cli.horizon, device) + # save the uncertainties + env.close() + + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() \ No newline at end of file diff --git a/scripts/imitation_learning/robomimic/play_ensemble.py b/scripts/imitation_learning/robomimic/play_ensemble.py index 2286052e3f11..275f2199b98c 100644 --- a/scripts/imitation_learning/robomimic/play_ensemble.py +++ b/scripts/imitation_learning/robomimic/play_ensemble.py @@ -29,6 +29,8 @@ # add argparse arguments parser = argparse.ArgumentParser(description="Evaluate robomimic policy for Isaac Lab environment.") +parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.") +parser.add_argument("--video_length", type=int, default=800, help="Length of the recorded video (in steps).") parser.add_argument( "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations." ) @@ -86,8 +88,14 @@ from isaaclab_tasks.utils import parse_env_cfg #from isaaclab.utils.logging_helper import LoggingHelper, ErrorType, LogType -from isaaclab.utils.math import matrix_from_quat, convert_quat +from isaaclab.utils.math import matrix_from_quat, convert_quat, quat_mul, euler_xyz_from_quat from evaluation import ensemble_uncertainty +from isaaclab.safety.switchingLogic import SwitchingLogic +from isaaclab.controllers import DifferentialIKController, DifferentialIKControllerCfg +from isaaclab.utils.math import subtract_frame_transforms, euler_xyz_from_quat +from isaaclab.managers import SceneEntityCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg + #import chills.tasks #from chills.tasks.mdp import upright_tilt_deg @@ -110,7 +118,7 @@ def rollout(policy, env, success_term, horizon, device): #print(f"obs_dict type: {type(obs_dict)},\nobs_dict contents:\n {obs_dict}") traj = dict(actions=[], obs=[], next_obs=[], sub_obs=[], uncertainties=[]) - + for i in range(horizon): # Prepare observations obs = copy.deepcopy(obs_dict["policy"]) @@ -210,7 +218,21 @@ def has_fallen(asset, threshold: float = -0.7, debug: bool = True) -> bool: return uprightness > threshold -def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, use_recovery=True, rollout_num=0): +# this took me too long to work out i needed lol + +def quaternion_to_euler(q_w, q_x, q_y, q_z): + # Roll (x-axis rotation) + roll = math.atan2(2 * (q_w * q_x + q_y * q_z), 1 - 2 * (q_x**2 + q_y**2)) + + # Pitch (y-axis rotation) + pitch = math.asin(2 * (q_w * q_y - q_z * q_x)) + + # Yaw (z-axis rotation) + yaw = math.atan2(2 * (q_w * q_z + q_x * q_y), 1 - 2 * (q_y**2 + q_z**2)) + + return roll, pitch, yaw + +def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, use_recovery=True, rollout_num=0): """Perform a single rollout of the policy in the environment. Args: @@ -232,29 +254,65 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, u traj = dict(actions=[], obs=[], next_obs=[], sub_obs=[], uncertainties=[], min_actions=[], max_actions=[], - time_taken=[], joint_pos=[], recovery=[]) + time_taken=[], joint_pos=[], recovery=[], failure=[], grasp=[], appr=[]) - joints_to_check = [0, 1, 2, 3, 4, 5, 6] - unsafe_windows_detected = {joint_num: 0 for joint_num in joints_to_check} - windows = {joint_num: deque(maxlen=parameters[joint_num]['window_size']) for joint_num in joints_to_check} - certain_timestep = 50 + ###### SETUP SWITCHING LOGIC #### + switchingLogic = SwitchingLogic(parameters, horizon=horizon) + + sim = env.unwrapped.sim + num_envs = env.num_envs + + certain_joint_positions = [] ### SET USE RECOVERY TO FALSE #### - use_recovery=False + use_recovery=True recovery_activated_during_rollout = 0 + # Set up recovery controller + robot = env.unwrapped.scene["robot"] + print(f"Unwrapped sim : {sim}") + ee_recovery_pos = torch.tensor([[0.5206, 0.0096, 0.3751]], device=device) + + ee_recovery_rot = torch.tensor([[ 0.6664, 0.0360, 0.7414, -0.0705]], device=device) + # lets change this into the 6 element tensor they are expecting + roll,pitch,yaw = euler_xyz_from_quat(ee_recovery_rot) + ee_recovery_goal = torch.cat([ee_recovery_pos, ee_recovery_rot], dim =-1) + print(f"I got this roll {roll}, pitch {pitch}, yaw {yaw} and full tensor {torch.cat([roll, pitch, yaw], dim=-1)}\n i need to add with {ee_recovery_pos}") + ee_rpy = torch.cat([roll, pitch, yaw], dim=-1) + ee_rpy = ee_rpy.unsqueeze(0) + ee_goal = torch.cat([ee_recovery_pos, ee_rpy], dim=1) + # Create controller + diff_ik_cfg = DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls") + diff_ik_controller = DifferentialIKController(diff_ik_cfg, num_envs=env.unwrapped.scene.num_envs, device=sim.device) + + # diff_ik_action_controller = DifferentialInverseKinematicsActionCfg( + # asset_name="robot", + # joint_names=["panda_joint.*"], + # body_name="panda_hand", + # controller=diff_ik_controller, + # scale=0.5, + # body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + # ) + robot_entity_cfg = SceneEntityCfg("robot", joint_names=["panda_joint.*"], body_names=["panda_hand"]) + robot_entity_cfg.resolve(env.unwrapped.scene) + if robot.is_fixed_base: + ee_jacobi_idx = robot_entity_cfg.body_ids[0] - 1 + else: + ee_jacobi_idx = robot_entity_cfg.body_ids[0] + #########SET TO FALSE ####### - recovery_mode = False + recovery_mode = True print("rollout recovery enabled ? : ", use_recovery) - recovery_duration = 100 - recovery_cooldown_duration = 100 # timesteps + + recovery_cooldown_duration = 300 # timesteps recovery_cooldown_timer = recovery_cooldown_duration recovery_cooldown_active = False recovery = [] + recovery_steps = 0 + print(f'Running for horizon {horizon}') for i in range(horizon): # Prepare observations - if bool(success_term.func(env, **success_term.params)[0]): - return True, traj, recovery_activated_during_rollout + obs = copy.deepcopy(obs_dict["policy"]) sub_obs = copy.deepcopy(obs_dict["subtask_terms"]) object = env.unwrapped.scene["object"] @@ -266,7 +324,8 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, u for subob in sub_obs: sub_obs[subob] = torch.squeeze(sub_obs[subob]) - + if obs['object_knocked']: + print("object knocked over") # Check if environment image observations if hasattr(env.cfg, "image_obs_list"): # Process image observations for robomimic inference @@ -281,104 +340,119 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, u traj["obs"].append(obs) traj["sub_obs"].append(sub_obs) + object=env.unwrapped.scene['object'] #tilt_deg = upright_tilt_deg(object.data.root_quat_w, object.data.default_root_state[:, 3:7]) #print("Tilt deg : ", tilt_deg.item()) - if recovery_mode: - print(f"Recovery Mode Started at Timestep {i}") - robot = env.unwrapped.scene["robot"] - tmp_pos = get_joint_pos(env) - # print("Temp, gripper a pos : ", tmp_pos[0][7]) - #print("Temp, gripper b pos : ", tmp_pos[0][8]) - safe_pos = None - if object_grasped(env): - # mid safe place - should help with the task of moving the beaker to the target - safe_pos = torch.tensor([[0.2932, 0.2640, -0.3573, -2.6099, -2.8971, 2.0274, 0.8245, tmp_pos[0][7], tmp_pos[0][8]]]).to(device) - else: - # origin - safe_pos = torch.tensor([[ 0.405,0.35,-0.22,-3,-2.85,math.pi/2,0.9, tmp_pos[0][7],tmp_pos[0][8]]]).to(device)# safe_pos = certain_joint_positions[-certain_timestep] - position_errors = deque(maxlen=50) - rec_i = 0 - - current = get_joint_pos(env) - target = safe_pos - steps = 200 # increase for slower motion - - for alpha in np.linspace(0, 1, steps): - recovery.append(True) - intermediate = (1 - alpha) * current + alpha * target - robot.set_joint_position_target(intermediate) + if recovery_mode and not recovery_cooldown_active: + print(f"[Recovery] Mode Timestep {i}") + + jacobian = robot.root_physx_view.get_jacobians()[:, ee_jacobi_idx, :, robot_entity_cfg.joint_ids] + ee_pose_w = robot.data.body_pose_w[:, robot_entity_cfg.body_ids[0]] + root_pose_w = robot.data.root_pose_w + joint_pos = robot.data.joint_pos[:, robot_entity_cfg.joint_ids] + + # current EE pose in base frame + ee_pos_b, ee_quat_b = subtract_frame_transforms( + root_pose_w[:, 0:3], root_pose_w[:, 3:7], + ee_pose_w[:, 0:3], ee_pose_w[:, 3:7] + ) + print(f"ee_pos_b {ee_pos_b}, ee_quat_b : {ee_quat_b}") + + # target EE pose (absolute, world frame → base frame) + target_pos_w = ee_recovery_pos # [1,3] + target_quat_w = ee_recovery_rot # [1,4] + target_pos_b, target_quat_b = subtract_frame_transforms( + root_pose_w[:, 0:3], root_pose_w[:, 3:7], + target_pos_w, target_quat_w + ) + print(f"target_pos_b {target_pos_b}, target_quat_b {target_quat_b}") + + # --- compute relative pose command (6D) --- + # translation delta + delta_pos = target_pos_b - ee_pos_b # [1,3] + print(f"Delta pos {delta_pos}") + # rotation delta (ΔR = R_target * R_currentᵀ) + delta_rot = target_quat_b - ee_quat_b + print(f"Delat Rot {delta_rot}") + # convert ΔR to euler angles (XYZ convention) + r,p,y = euler_xyz_from_quat(delta_rot, wrap_to_2pi=False) + print(f"[DEBUG] Delat R {r}, P {p}, Y {y}") + delta_rpy = torch.cat([r,p,y], dim=-1) + delta_rpy = torch.unsqueeze(delta_rpy,0) + print(f"[DEBUG] RPY {delta_rpy}") + # --- build relative command --- + ee_goal = torch.cat([delta_pos, delta_rpy], dim=-1) # [1,6] + + + # debug info + print(f"[DEBUG] ee_goal (Δx,Δy,Δz,Δr,Δp,Δy): {ee_goal}") + print(f"[DEBUG] Current pos_b: {ee_pos_b}") + print(f"[DEBUG] Target pos_b: {target_pos_b}") + + # --- send command to controller --- + diff_ik_controller.set_command(ee_goal, ee_pos_b, ee_quat_b) + + # compute joint targets + joint_pos_des = diff_ik_controller.compute(ee_pos_b, ee_quat_b, jacobian, joint_pos) + + # (optional) denormalize actions if necessary + if args_cli.norm_factor_min is not None and args_cli.norm_factor_max is not None: + actions = ((actions + 1) * (args_cli.norm_factor_max - args_cli.norm_factor_min)) / 2 + args_cli.norm_factor_min + + # --- execute recovery movement --- + pos_err = torch.norm(target_pos_b - ee_pos_b) + actions=joint_pos_des + metrics = ensemble_uncertainty(ensemble, obs) + metrics['min']=actions + metrics['max']=actions + uncertainty = 0 + + if pos_err > 1e-3: + robot.set_joint_position_target(joint_pos_des, joint_ids=robot_entity_cfg.joint_ids) env.scene.write_data_to_sim() - env.sim.step() - if bool(success_term.func(env, **success_term.params)[0]): - return True, traj, recovery_activated_during_rollout - - - - print(f"Recovery Mode Ended") - print("Recovery Cooldown Activated") - recovery_mode = False - recovery_cooldown_active = True - obs_dict, _, terminated, truncated, _ = env.step(torch.tensor([0 for _ in range(7)]).to(device).view(1, env.action_space.shape[1])) - obs = copy.deepcopy(obs_dict["policy"]) - for ob in obs: - obs[ob] = torch.squeeze(obs[ob]) - ensemble_uncertainty(ensemble, obs) + obs_dict, _, terminated, truncated, _ = env.step(joint_pos_des) + else: + print(f"[Recovery] Target reached — ending recovery mode.") + recovery_mode = False + recovery_cooldown_active = True + print("[Recovery] Cooldown Activated.") + + else: + env.unwrapped.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + ) + recovery_steps=0 # Calculate uncertainty using the ensemble recovery.append(False) - if bool(success_term.func(env, **success_term.params)[0]): - return True, traj, recovery_activated_during_rollout - ## detect if we have tipped the object + ## detect if we have tipped the object metrics = ensemble_uncertainty(ensemble, obs) # Get the std and mean action uncertainty = metrics['variance'] actions = metrics['mean'] - # Add the uncertainty to all joint windows - for joint_num, unc in enumerate(uncertainty): - windows[joint_num].append(unc) - for joint_num in joints_to_check: - unc_threshold = parameters[joint_num]['unc_threshold'] - peaks = sum(1 for curr_unc in windows[joint_num] if curr_unc > unc_threshold) - if peaks > parameters[joint_num]['max_peaks']: - unsafe_windows_detected[joint_num] += 1 + # uncert went here - - for joint_num in joints_to_check: - ## less worried after the object grasped - if unsafe_windows_detected[joint_num] > parameters[joint_num]['max_uncertain_windows'] and not recovery_cooldown_active: - print(f"Joint {joint_num} is uncertain at timestep {i}, uncertainty is {uncertainty[joint_num]}") - unsafe_windows_detected[joint_num] = 0 - if use_recovery: - recovery_mode = True - recovery_activated_during_rollout += 1 - # if object_grasped(env): - - # else : - # ## more strict when grasping - # if (unsafe_windows_detected[joint_num] > (parameters[joint_num]['max_uncertain_windows']-1)) and not recovery_cooldown_active: - # print(f"Object not grapsed, using more strict uncertainty Joint {joint_num} is uncertain at timestep {i}, uncertainty is {uncertainty[joint_num]}") - # unsafe_windows_detected[joint_num] = 0 - # if obs_dict['subtask_terms']['appr_goal'].item(): - # # then we are placing the object , do not recover - # print("not activating recovery, object at goal") - # elif use_recovery: - # recovery_mode = True - # recovery_activated_during_rollout += 1 - - if use_recovery and not recovery_cooldown_active: - # print("not holding object, check if not hit it") - # print("observed tilt " , obs_dict['policy']['object_tilt']) - # if obs_dict['policy']['object_tilt'].item() > 5.0: - # print("Ucertainties when tilt : ",uncertainty) - # print("Object tilted, activating no tilt recovery ") - # print("observed tilt " , obs_dict['policy']['object_tilt']) - recovery_mode =True + + if switchingLogic.check(uncertainty, i) and not recovery_cooldown_active: + if use_recovery : + recovery_mode = True recovery_activated_during_rollout+=1 + + #check halfway thorough, if we havent managed grasp, lets reset and try again + if switchingLogic.halfway_check(i,sub_obs['grasp'].item()): + if use_recovery and not recovery_cooldown_active: + recovery_mode = True + if recovery_cooldown_active: if object_grasped(env): @@ -404,39 +478,62 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, u actions = ( (actions + 1) * (args_cli.norm_factor_max - args_cli.norm_factor_min) ) / 2 + args_cli.norm_factor_min - + # print(f"before shaping action : {actions}") actions = actions.to(device=device).view(1, env.action_space.shape[1]) # print(f"------------------------------") - # print(f"Policy action : {actions.tolist()}") + # print(f"Policy action {actions.shape} : {actions}") # Apply actions obs_dict, _, terminated, truncated, _ = env.step(actions) - obs = obs_dict["policy"] - sub_obs = obs_dict["subtask_terms"] - # print(f"Obs change : {obs}") - # Record trajectory - traj["actions"].append(actions.tolist()) - traj["next_obs"].append(obs) - traj['uncertainties'].append(uncertainty) - traj['max_actions'].append(metrics['max']) - traj['min_actions'].append(metrics['min']) - traj['time_taken'].append(metrics['time_taken']) - traj['joint_pos'].append(obs['abs_joint_pos']) - traj['recovery'].append(recovery) - + obs = obs_dict["policy"] + sub_obs = obs_dict["subtask_terms"] + # print(f"Obs change : {obs}") + # Record trajectory + traj["actions"].append(actions.tolist()) + traj["next_obs"].append(obs) + traj['uncertainties'].append(uncertainty) + traj['max_actions'].append(metrics['max']) + traj['min_actions'].append(metrics['min']) + traj['time_taken'].append(metrics['time_taken']) + traj['joint_pos'].append(obs['abs_joint_pos']) + traj['recovery'].append(recovery) - # Check if rollout was successful - if bool(success_term.func(env, **success_term.params)[0]): - return True, traj, recovery_activated_during_rollout - elif terminated or truncated: - return False, traj, recovery_activated_during_rollout + # Check if rollout was successful + if bool(success_term.func(env, **success_term.params)[0]): + traj['failure'].append(" ") + grasp = torch.any(sub_obs['grasp']) + traj['grasp'].append(grasp.item()) + appr = torch.any(sub_obs['appr_goal']) + traj['appr'].append(appr.item()) + print(f"grasp : {traj['grasp']}, appr : {traj['appr']}") + # print(f"grasp : {grasp.item()}, appr : {appr.item()}") + return True, traj, recovery_activated_during_rollout + + elif terminated or truncated: + + grasp = torch.any(sub_obs['grasp']) + traj['grasp'].append(grasp.item()) + appr = torch.any(sub_obs['appr_goal']) + traj['appr'].append(appr.item()) + #print(f"grasp : {traj['grasp']}, appr : {traj['appr']}") + if obs['object_knocked']: + + #if the failure was caused by object collision + traj['failure'].append("knocked") + else: + #if failure due to timeout, record timeout + traj['failure'].append("timeout") + return False, traj, recovery_activated_during_rollout #print("REcovery : ", traj["recovery"]) + traj['failure'].append("timeout") + grasp = torch.any(sub_obs['grasp']) + traj['grasp'].append(grasp.item()) + appr = torch.any(sub_obs['appr_goal']) + traj['appr'].append(appr.item()) return False, traj, recovery_activated_during_rollout -def safety(): - print(f"I'm Uncertain. {random.randint(0, 100)}") - return True + def rollout_transformer(policy, env, success_term, horizon, device): """Perform a single rollout of the policy in the environment, supporting sequence-based models.""" @@ -584,19 +681,6 @@ def rollout_transformer(policy, env, success_term, horizon, device): return False, traj -# ./isaaclab.sh -p scripts/imitation_learning/robomimic/play.py \ -# --device cuda --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --num_rollouts 50 \ -# --checkpoint /logs/docs/Models/bc/model1/Isaac-Stack-Cube-Franka-IK-Rel-v0/bc_rnn_low_dim_franka_stack/20250715152224/models/model_epoch_2000.pth - - -# ./isaaclab.sh -p scripts/imitation_learning/robomimic/play.py --device cuda --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --num_rollouts 10 --checkpoint logs/docs/Models/bc/model2/Isaac-Stack-Cube-Franka-IK-Rel-v0/bc_rnn_low_dim_franka_stack/20250715152538/models/model_epoch_2000.pth --headless - - - -# ./isaaclab.sh -p scripts/imitation_learning/robomimic/train.py \ -# --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --algo bc \ -# --dataset ./docs/training_data/generated_dataset_split.hdf5 --logdir ./logs/docs/Models/bc/model8/ - def load_ensemble(device, ensemble_path): @@ -636,8 +720,7 @@ def main(): #create a log handler # loghelper = LoggingHelper() - # Set termination conditions - env_cfg.terminations.time_out = None + # Disable recorder env_cfg.recorders = None @@ -646,10 +729,14 @@ def main(): success_term = env_cfg.terminations.success env_cfg.terminations.success = None + # Get timeout signal + timeout = env_cfg.terminations.time_out + #env_cfg.terminations.time_out = None + # Create environment env = gym.make(args_cli.task, cfg=env_cfg).unwrapped - # Set seed + # Set seed9 torch.manual_seed(args_cli.seed) env.seed(args_cli.seed) @@ -657,151 +744,51 @@ def main(): device = TorchUtils.get_torch_device(try_to_use_cuda=True) # load the stack cube ensemble - # stack_cube_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/stack_cube_ensemble.txt') + # stack_cube_ensemble = load_ensembl*--------------e(device, ensemble_path='scripts/imitation_learning/robomimic/stack_cube_ensemble.txt') #pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/ensembles.txt') - pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/high_qual_ensembles.txt') + pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/med_ensembles.txt') # pick_place_ensemble_30 = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/pick_place_ensemble_30_paths.txt') - + # Lets set these to the 0.99 confidence parameters = { - 'stack_cube': { - 6: { - 'unc_threshold': 0.05, - 'max_peaks': 15, - 'window_size': 30, - 'max_uncertain_windows': 3 - }, - 5: { - 'unc_threshold': 0.06, - 'max_peaks': 2, - 'window_size': 10, - 'max_uncertain_windows': 3 - }, - - 4: { - 'unc_threshold': 0.04, - 'max_peaks': 2, - 'window_size': 10, - 'max_uncertain_windows': 3 - }, - 3: { - 'unc_threshold': 0.04, - 'max_peaks': 2, - 'window_size': 10, - 'max_uncertain_windows': 3 + 'beaker_lift' :{ + 0 : { + "confidence_level": 0.017165569182596162, + "window_size": 5, + "max_peaks": 5 }, - 2: { - 'unc_threshold': 0.04, - 'max_peaks': 2, - 'window_size': 10, - 'max_uncertain_windows': 3 + 1 : { + "confidence_level": 0.024839555704716736, + "window_size": 12, + "max_peaks": 4 }, - 1: { - 'unc_threshold': 0.04, - 'max_peaks': 2, - 'window_size': 10, - 'max_uncertain_windows': 3 + 2 : { + "confidence_level": 0.022856649849482057, + "window_size": 12, + "max_peaks": 5 }, - 0: { - 'unc_threshold': 0.04, - 'max_peaks': 2, - 'window_size': 10, - 'max_uncertain_windows': 3 - } - }, - - 'pick_place': { - 6: { - 'unc_threshold': 0.05, - 'max_peaks': 15, - 'window_size': 30, - 'max_uncertain_windows': 3 - }, - 5: { - 'unc_threshold': 0.02, - 'max_peaks': 2, - 'window_size': 10, - 'max_uncertain_windows': 1 - }, - - 4: { - 'unc_threshold': 0.03, - 'max_peaks': 2, - 'window_size': 10, - 'max_uncertain_windows': 3 + 3 : { + "confidence_level": 0.03564607457876469, + "window_size": 9, + "max_peaks": 5 }, - 3: { - 'unc_threshold': 0.03, - 'max_peaks': 2, - 'window_size': 10, - 'max_uncertain_windows': 3 + 4 : { + "confidence_level": 0.03087440802734071, + "window_size": 10, + "max_peaks": 3 }, - 2: { - 'unc_threshold': 0.02, - 'max_peaks': 2, - 'window_size': 10, - 'max_uncertain_windows': 3 + 5 : { + "confidence_level": 0.02323877457883784, + "window_size": 5, + "max_peaks": 4 }, - 1: { - 'unc_threshold': 0.03, - 'max_peaks': 2, - 'window_size': 10, - 'max_uncertain_windows': 3 + 6 : { + "confidence_level": 0.8432899916370529, + "window_size": 13, + "max_peaks": 4 }, - 0: { - 'unc_threshold': 0.03, - 'max_peaks': 2, - 'window_size': 10, - 'max_uncertain_windows': 3 - } - }, - 'stack_beaker': { - 6: { - 'unc_threshold': 0.05, - 'max_peaks': 15, - 'window_size': 30, - 'max_uncertain_windows': 3 - }, - 5: { - 'unc_threshold': 0.02, - 'max_peaks': 2, - 'window_size': 10, - 'max_uncertain_windows': 3 - }, - - 4: { - 'unc_threshold': 0.03, - 'max_peaks': 2, - 'window_size': 10, - 'max_uncertain_windows': 3 - }, - 3: { - 'unc_threshold': 0.03, - 'max_peaks': 5, - 'window_size': 10, - 'max_uncertain_windows': 3 - }, - 2: { - 'unc_threshold': 0.02, - 'max_peaks': 2, - 'window_size': 10, - 'max_uncertain_windows': 3 - }, - 1: { - 'unc_threshold': 0.03, - 'max_peaks': 5, - 'window_size': 10, - 'max_uncertain_windows': 3 - }, - 0: { - 'unc_threshold': 0.03, - 'max_peaks': 2, - 'window_size': 10, - 'max_uncertain_windows': 3 - }, - } } model_arch = "BC_RNN_GMM" @@ -827,11 +814,7 @@ def main(): os.makedirs(uncertainty_results_path, exist_ok=True) os.makedirs(trajectory_results_path, exist_ok=True) - # try: - # os.remove(rollout_log_path) - # except FileNotFoundError as filenotfounderror: - # pass - + # clear files clear_files( uncertainties_path, actions_path, @@ -839,32 +822,22 @@ def main(): time_taken_path, recovery_activated_during_rollout_path ) - #clear uncertainties file - with open(uncertainties_path, 'w') as file: - pass - with open(actions_path, 'w') as file: - pass - with open(min_actions_path, 'w') as file: - pass - with open(max_actions_path, 'w') as file: - pass - with open(time_taken_path, 'w') as file: - pass - with open(recovery_activated_during_rollout_path, 'w') as file: - pass - # clear rollout logger file - #with open(loghelper.namefile, 'w') as file: - # pass - # Run policy results = [] + failed_no_intervention =[] + intervention = [] + intervention_success = [] + intervention_failure = [] for trial in range(args_cli.num_rollouts): print(f"[INFO] Starting trial {trial}") - terminated, traj, recovery_activated_during_rollout = rollout_ensemble(pick_place_ensemble[:args_cli.ensemble_size], env, success_term, args_cli.horizon, device, parameters['pick_place'], use_recovery=args_cli.use_recovery, rollout_num=trial) + terminated, traj, recovery_activated_during_rollout = rollout_ensemble(pick_place_ensemble[:args_cli.ensemble_size], env, success_term, args_cli.horizon, device, parameters['beaker_lift'], use_recovery=args_cli.use_recovery, rollout_num=trial) # save the uncertainties print("Finished rollout, recovery needed : ", recovery_activated_during_rollout) #print("actions shape : ", traj['actions']) + + if not terminated: + print(f"failure : {traj['failure']}") for j in range(7): with open(f"docs/rollouts/rollout_logging_no_recovery_joint{j}.txt", "a") as file: #file.write(f"Logs for joint {j}\n") @@ -876,53 +849,53 @@ def main(): uncert = str(traj['uncertainties'][i].tolist()[j]) #print("joint pos : ", str(traj['joint_pos'][i][0].tolist()[j])) joint_pos = str(traj['joint_pos'][i][0].tolist()[j]) - recovery = str(traj['recovery'][0][i]) + recovery = "False"#str(traj['recovery'][0][i]) # print("writing recovery : ", recovery) line = f"{i} : {act} : {max} : {min} : {uncert} : {joint_pos}: {recovery}: {terminated}\n" file.write(line) - - + # this logs the outcome of the rollout - the failure methods for j in range(7): joint_uncert = [t[j].item() for t in traj['uncertainties']] with open(f"docs/rollouts/rollout_uncerts_joint{j}.txt", "a") as f: line = ",".join(["{:.10f}".format(v) for v in joint_uncert]) f.write(line + f" : {terminated} \n") + # for i in range(len(traj['failure'])): + with open(f"docs/rollouts/rollout_outcomes.txt", "a") as file: + if terminated: + # print(f"grasp : {traj['grasp']}, appr : {traj['appr']}") + #trialnum : outcome : recovery needed ? : failure mode : grasp subtask : appr subtask + line=f"{trial}: {terminated} : {recovery_activated_during_rollout} : : {True} : {True} \n" + else : + grasp = traj['grasp'][0] + appr = traj['appr'][0] + print(f"Subatask : {grasp} , {appr}") + line=f"{trial} : {terminated} : {recovery_activated_during_rollout} : {traj['failure'][0]} : {grasp} : {appr}\n" + # print(f"grasp : {traj['grasp'][0]}, appr : {traj['appr'][0]}") + file.write(line) - with open(uncertainties_path, 'a') as file: - for i, var in enumerate(traj['uncertainties']): - line = " ".join([str(v.item()) for v in var]) + f" {terminated}\n" - file.write(f"{str(i)} {line}") - # save the actions - with open(actions_path, 'a') as file: - for i, var in enumerate(traj['actions']): - line = " ".join([str(v) for v in var[0]]) + f" {terminated}\n" - file.write(f"{str(i)} {line}") - # save the max actions - with open(max_actions_path, 'a') as file: - for i, var in enumerate(traj['max_actions']): - line = " ".join([str(v.item()) for v in var[0]]) + f" {terminated}\n" - file.write(f"{str(i)} {line}") - # save the min actions - with open(min_actions_path, 'a') as file: - for i, var in enumerate(traj['min_actions']): - line = " ".join([str(v.item()) for v in var[0]]) + f" {terminated}\n" - file.write(f"{str(i)} {line}") - # save time taken - with open(time_taken_path, 'a') as file: - for i, time_taken in enumerate(traj['time_taken']): - line = f"{str(time_taken)} {terminated}\n" - file.write(f"{str(i)} {line}") - # save recovery activated - with open("recovery_actions.txt", 'a') as file: - file.write(f"{trial} {recovery_activated_during_rollout} {terminated}\n") - - + results.append(terminated) + if not terminated: + if recovery_activated_during_rollout <=0: + failed_no_intervention.append(1) + + if recovery_activated_during_rollout > 0: + intervention.append(1) + if terminated: + intervention_success.append(1) + else: + intervention_failure.append(1) + else: + intervention.append(0) + print(f'Current rate {results.count(True)/len(results)}') print(f"[INFO] Trial {trial}: {terminated}\n") #loghelper.stopEpoch(trial) print(f"\nSuccessful trials: {results.count(True)}, out of {len(results)} trials") print(f"Success rate: {results.count(True) / len(results)}") + print(f'Failed runs with no intervention : {sum(failed_no_intervention)}') + print(f"Intervention rate : {sum(intervention)/len(intervention)}. Of which success : {sum(intervention_success)}, of which failed {sum(intervention_failure)}") + print(f"Trial Results: {results}\n") diff --git a/scripts/tutorials/05_controllers/run_diff_ik.py b/scripts/tutorials/05_controllers/run_diff_ik.py index 606a2b8b1c0d..d0e76250f208 100644 --- a/scripts/tutorials/05_controllers/run_diff_ik.py +++ b/scripts/tutorials/05_controllers/run_diff_ik.py @@ -25,7 +25,7 @@ # add argparse arguments parser = argparse.ArgumentParser(description="Tutorial on using the differential IK controller.") parser.add_argument("--robot", type=str, default="franka_panda", help="Name of the robot.") -parser.add_argument("--num_envs", type=int, default=128, help="Number of environments to spawn.") +parser.add_argument("--num_envs", type=int, default=1, help="Number of environments to spawn.") # append AppLauncher cli args AppLauncher.add_app_launcher_args(parser) # parse the arguments @@ -49,6 +49,7 @@ from isaaclab.utils import configclass from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR from isaaclab.utils.math import subtract_frame_transforms +from isaaclab.assets import RigidObjectCfg, ArticulationCfg ## # Pre-defined configs @@ -82,7 +83,21 @@ class TableTopSceneCfg(InteractiveSceneCfg): # articulation if args_cli.robot == "franka_panda": - robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot", init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + + "panda_joint1": 0.3281, + "panda_joint2": -0.3684, + "panda_joint3": -0.2787, + "panda_joint4": -2.6138, + "panda_joint5": -2.7527, + "panda_joint6": 2.4991, # +90° → keeps hand level + "panda_joint7": 0.3331, + "panda_finger_joint1": 0.04, # open gripper + "panda_finger_joint2": 0.04, + } + ), + ) elif args_cli.robot == "ur10": robot = UR10_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") else: @@ -108,7 +123,7 @@ def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): # Define goals for the arm ee_goals = [ [0.5, 0.5, 0.7, 0.707, 0, 0.707, 0], - [0.5, -0.4, 0.6, 0.707, 0.707, 0.0, 0.0], + [0.5206, 0.0096, 0.3751, 0.6664, 0.0360, 0.7414, -0.0705], [0.5, 0, 0.5, 0.0, 1.0, 0.0, 0.0], ] ee_goals = torch.tensor(ee_goals, device=sim.device) @@ -141,7 +156,7 @@ def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): # Simulation loop while simulation_app.is_running(): # reset - if count % 150 == 0: + if count % 300 == 0: # reset time count = 0 # reset joint state diff --git a/scripts/tutorials/05_controllers/run_diff_ik_rel.py b/scripts/tutorials/05_controllers/run_diff_ik_rel.py new file mode 100644 index 000000000000..3624bb2b3a64 --- /dev/null +++ b/scripts/tutorials/05_controllers/run_diff_ik_rel.py @@ -0,0 +1,250 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +This script demonstrates how to use the differential inverse kinematics controller with the simulator. + +The differential IK controller can be configured in different modes. It uses the Jacobians computed by +PhysX. This helps perform parallelized computation of the inverse kinematics. + +.. code-block:: bash + + # Usage + ./isaaclab.sh -p scripts/tutorials/05_controllers/run_diff_ik.py + +""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Tutorial on using the differential IK controller.") +parser.add_argument("--robot", type=str, default="franka_panda", help="Name of the robot.") +parser.add_argument("--num_envs", type=int, default=1, help="Number of environments to spawn.") +# append AppLauncher cli args + +parser.add_argument("--num_envs", type=int, default=1, help="Number of environments to simulate.") + +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import torch +import robomimic.utils.torch_utils as TorchUtils +import isaaclab.sim as sim_utils +from isaaclab.assets import AssetBaseCfg +from isaaclab.controllers import DifferentialIKController, DifferentialIKControllerCfg +from isaaclab.managers import SceneEntityCfg +from isaaclab.markers import VisualizationMarkers +from isaaclab.markers.config import FRAME_MARKER_CFG +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils.math import subtract_frame_transforms +from isaaclab.assets import RigidObjectCfg, ArticulationCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +## +# Pre-defined configs +## +import gymnasium as gym +import torch +from isaaclab_assets import FRANKA_PANDA_HIGH_PD_CFG, UR10_CFG # isort:skip +from isaaclab_tasks.utils import parse_env_cfg +from isaaclab_tasks.manager_based.manipulation.lift.lift_env_cfg import LiftEnvCfg +# @configclass +# class TableTopSceneCfg(InteractiveSceneCfg): +# """Configuration for a cart-pole scene.""" + +# # ground plane +# ground = AssetBaseCfg( +# prim_path="/World/defaultGroundPlane", +# spawn=sim_utils.GroundPlaneCfg(), +# init_state=AssetBaseCfg.InitialStateCfg(pos=(0.0, 0.0, -1.05)), +# ) + +# # lights +# dome_light = AssetBaseCfg( +# prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) +# ) + +# # mount +# table = AssetBaseCfg( +# prim_path="{ENV_REGEX_NS}/Table", +# spawn=sim_utils.UsdFileCfg( +# usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/Stand/stand_instanceable.usd", scale=(2.0, 2.0, 2.0) +# ), +# ) + +# # articulation +# if args_cli.robot == "franka_panda": +# robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot", init_state=ArticulationCfg.InitialStateCfg( +# joint_pos={ + +# "panda_joint1": 0.3281, +# "panda_joint2": -0.3684, +# "panda_joint3": -0.2787, +# "panda_joint4": -2.6138, +# "panda_joint5": -2.7527, +# "panda_joint6": 2.4991, # +90° → keeps hand level +# "panda_joint7": 0.3331, +# "panda_finger_joint1": 0.04, # open gripper +# "panda_finger_joint2": 0.04, +# } +# ), +# ) +# elif args_cli.robot == "ur10": +# robot = UR10_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") +# else: +# raise ValueError(f"Robot {args_cli.robot} is not supported. Valid: franka_panda, ur10") + + +def run_simulator(env): + """Runs the simulation loop.""" + # Extract scene entities + # note: we only do this here for readability. + robot = scene["robot"] + diff_ik_cfg = DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls") + diff_ik_controller = DifferentialIKController(diff_ik_cfg, num_envs=env.unwrapped.scene.num_envs, device=sim.device) + + diff_ik_action_controller = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=diff_ik_controller, + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + ) + # Create controller + # diff_ik_cfg = DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls") + #diff_ik_controller = DifferentialIKController(diff_ik_cfg, num_envs=scene.num_envs, device=sim.device) + + # Markers + frame_marker_cfg = FRAME_MARKER_CFG.copy() + frame_marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + ee_marker = VisualizationMarkers(frame_marker_cfg.replace(prim_path="/Visuals/ee_current")) + goal_marker = VisualizationMarkers(frame_marker_cfg.replace(prim_path="/Visuals/ee_goal")) + + # Define goals for the arm + ee_goals = [ + [0.5, 0.5, 0.7, 0.707, 0, 0.707, 0], + [0.5206, 0.0096, 0.3751, 0.6664, 0.0360, 0.7414, -0.0705], + [0.5, 0, 0.5, 0.0, 1.0, 0.0, 0.0], + ] + ee_goals = torch.tensor(ee_goals, device=sim.device) + # Track the given command + current_goal_idx = 0 + # Create buffers to store actions + ik_commands = torch.zeros(scene.num_envs, diff_ik_controller.action_dim, device=robot.device) + ik_commands[:] = ee_goals[current_goal_idx] + + # Specify robot-specific parameters + if args_cli.robot == "franka_panda": + robot_entity_cfg = SceneEntityCfg("robot", joint_names=["panda_joint.*"], body_names=["panda_hand"]) + elif args_cli.robot == "ur10": + robot_entity_cfg = SceneEntityCfg("robot", joint_names=[".*"], body_names=["ee_link"]) + else: + raise ValueError(f"Robot {args_cli.robot} is not supported. Valid: franka_panda, ur10") + # Resolving the scene entities + robot_entity_cfg.resolve(scene) + # Obtain the frame index of the end-effector + # For a fixed base robot, the frame index is one less than the body index. This is because + # the root body is not included in the returned Jacobians. + if robot.is_fixed_base: + ee_jacobi_idx = robot_entity_cfg.body_ids[0] - 1 + else: + ee_jacobi_idx = robot_entity_cfg.body_ids[0] + + # Define simulation stepping + sim_dt = sim.get_physics_dt() + count = 0 + # Simulation loop + while simulation_app.is_running(): + # reset + if count % 300 == 0: + # reset time + count = 0 + # reset joint state + joint_pos = robot.data.default_joint_pos.clone() + joint_vel = robot.data.default_joint_vel.clone() + robot.write_joint_state_to_sim(joint_pos, joint_vel) + robot.reset() + # reset actions + ik_commands[:] = ee_goals[current_goal_idx] + joint_pos_des = joint_pos[:, robot_entity_cfg.joint_ids].clone() + # reset controller + diff_ik_controller.reset() + diff_ik_controller.set_command(ik_commands) + # change goal + current_goal_idx = (current_goal_idx + 1) % len(ee_goals) + else: + # obtain quantities from simulation + jacobian = robot.root_physx_view.get_jacobians()[:, ee_jacobi_idx, :, robot_entity_cfg.joint_ids] + ee_pose_w = robot.data.body_pose_w[:, robot_entity_cfg.body_ids[0]] + root_pose_w = robot.data.root_pose_w + joint_pos = robot.data.joint_pos[:, robot_entity_cfg.joint_ids] + # compute frame in root frame + ee_pos_b, ee_quat_b = subtract_frame_transforms( + root_pose_w[:, 0:3], root_pose_w[:, 3:7], ee_pose_w[:, 0:3], ee_pose_w[:, 3:7] + ) + # compute the joint commands + joint_pos_des = diff_ik_controller.compute(ee_pos_b, ee_quat_b, jacobian, joint_pos) + + # apply actions + robot.set_joint_position_target(joint_pos_des, joint_ids=robot_entity_cfg.joint_ids) + scene.write_data_to_sim() + # perform step + sim.step() + # update sim-time + count += 1 + # update buffers + scene.update(sim_dt) + + # obtain quantities from simulation + ee_pose_w = robot.data.body_state_w[:, robot_entity_cfg.body_ids[0], 0:7] + # update marker positions + ee_marker.visualize(ee_pose_w[:, 0:3], ee_pose_w[:, 3:7]) + goal_marker.visualize(ik_commands[:, 0:3] + scene.env_origins, ik_commands[:, 3:7]) + + +def main(): + """Main function.""" + # Load kit helper + + env_cfg: LiftEnvCfg = parse_env_cfg( + "Dev-IK-Rel-v1", + device=args_cli.device, + num_envs=args_cli.num_envs, + use_fabric=not args_cli.disable_fabric, + ) + # create environment + env = gym.make("Isaac-Lift-Cube-Franka-IK-Abs-v0", cfg=env_cfg) + # reset environment at start + env.reset() + # Set main camera + sim.set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0]) + # Design scene + + # Play the simulator + sim.reset() + # Now we are ready! + print("[INFO]: Setup complete...") + # Run the simulator + run_simulator(env) + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/source/isaaclab/isaaclab/controllers/differential_ik.py b/source/isaaclab/isaaclab/controllers/differential_ik.py index a9bbfdb160c7..0a9d77d4216b 100644 --- a/source/isaaclab/isaaclab/controllers/differential_ik.py +++ b/source/isaaclab/isaaclab/controllers/differential_ik.py @@ -117,6 +117,8 @@ def set_command( ValueError: If the command type is ``pose_rel`` and either :attr:`ee_pos` or :attr:`ee_quat` is None. """ # store command + print(f"[DEBUG] command : {command}") + self._command[:] = command # compute the desired end-effector pose if self.cfg.command_type == "position": @@ -141,6 +143,7 @@ def set_command( "Neither end-effector position nor orientation can be None for `pose_rel` command type!" ) self.ee_pos_des, self.ee_quat_des = apply_delta_pose(ee_pos, ee_quat, self._command) + print(f"Calculated pos and quat {self.ee_pos_des}, {self.ee_quat_des}") else: self.ee_pos_des = self._command[:, 0:3] self.ee_quat_des = self._command[:, 3:7] diff --git a/source/isaaclab/isaaclab/envs/mdp/terminations.py b/source/isaaclab/isaaclab/envs/mdp/terminations.py index a13ce9bb4a2d..ac472a923574 100644 --- a/source/isaaclab/isaaclab/envs/mdp/terminations.py +++ b/source/isaaclab/isaaclab/envs/mdp/terminations.py @@ -29,6 +29,8 @@ def time_out(env: ManagerBasedRLEnv) -> torch.Tensor: """Terminate the episode when the episode length exceeds the maximum episode length.""" + if env.episode_length_buf >= env.max_episode_length: + print("Timeout") return env.episode_length_buf >= env.max_episode_length diff --git a/source/isaaclab/isaaclab/safety/__init__.py b/source/isaaclab/isaaclab/safety/__init__.py new file mode 100644 index 000000000000..b02f195e7d31 --- /dev/null +++ b/source/isaaclab/isaaclab/safety/__init__.py @@ -0,0 +1 @@ +from .switchingLogic import SwitchingLogic \ No newline at end of file diff --git a/source/isaaclab/isaaclab/safety/switchingLogic.py b/source/isaaclab/isaaclab/safety/switchingLogic.py new file mode 100644 index 000000000000..f2aa069102c1 --- /dev/null +++ b/source/isaaclab/isaaclab/safety/switchingLogic.py @@ -0,0 +1,47 @@ +from collections import deque +import statistics +import numpy as np + +class SwitchingLogic: + def __init__(self, parameters, joints_to_check:list[int] =[0, 1, 2, 3, 4, 5, 6] , window_size: int = 10, peak_limit: int =2, horizon:int = 500): + self.params = parameters + self.joints = joints_to_check + self.window_size : int = window_size + self.horizon:int =horizon + self.traj = [[0.0]*horizon for _ in range(len(joints_to_check))] + self.windows= self._calc_windows() + self.unsafe_windows = {joint_num : 0 for joint_num in self.joints} + self.peak_limit = peak_limit + self.window_confidence_level = 0.669 + + + def _calc_windows(self)-> list[deque] : + # return a list of deque objects, one for each joint + return [deque(maxlen=self.window_size) for _ in range(len(self.joints))] + + def halfway_check(self, step:int=0, grasp_state:bool=True): + #check that the subtask has been comepleted + if (step==(round(self.horizon/2))) and not grasp_state: + print(f'I got halfway and hadnt done subtask, lets try again') + #then we are halfway through and lost, try again + return True + return False + + + def check(self, uncertainties, step) -> bool: + # 1. Update windows + for joint_num, unc in enumerate(uncertainties): + # Check for bounds if uncertainties is smaller than self.joints + self.traj[joint_num][step]=unc.item() + if step > self.params[joint_num]['window_size']: + + test_window = self.traj[joint_num][step-self.params[joint_num]['window_size']:step] + peaks = sum(1 for x in test_window if x>= self.params[joint_num]['confidence_level']) + # print(f'joint {joint_num}, window: {test_window}') + # print(f"manually found peaks : {peaks}") + if peaks>=self.params[joint_num]['max_peaks']: + print(f"Uncertaity in joint {joint_num} triggered") + return True + + + return False \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py new file mode 100755 index 000000000000..f63cbb50b0bd --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py @@ -0,0 +1,82 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass +from isaaclab.assets import RigidObjectCfg, ArticulationCfg +from isaaclab.managers import EventTermCfg as EventTerm +from . import dev_env_cfg +import math +from isaaclab.managers import SceneEntityCfg +from isaaclab_assets.glassware.glassware import ChemistryGlassware +from isaaclab_tasks.manager_based.manipulation.cube_lift import mdp +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + + +@configclass +class FrankaDevEnvCfg(dev_env_cfg.FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # put the beaker on the stir plate + glassware = ChemistryGlassware() + self.scene.stirplate = glassware.stirplate(pos=[0.5, 0.0, 0.01]) + + self.events.reset_object_position = EventTerm( + func=mdp.reset_multiple_root_state_uniform, + mode="reset", + params={ + "pose_range": {"x": (0, 0.2), "y": (0, 0.25), "z": (0.02, 0.02)}, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("object", body_names="Object"), + "asset2_cfg" : SceneEntityCfg("stirplate") + }, + ) + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace( + prim_path="{ENV_REGEX_NS}/Robot", + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + + "panda_joint1": 0.3281, + "panda_joint2": -0.3684, + "panda_joint3": -0.2787, + "panda_joint4": -2.6138, + "panda_joint5": -2.7527, + "panda_joint6": 2.4991, # +90° → keeps hand level + "panda_joint7": 0.3331, + "panda_finger_joint1": 0.04, # open gripper + "panda_finger_joint2": 0.04, + } + ), + ) + # replace with relative position controller + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + ) + + + + + +@configclass +class FrankaCubeEnvCfg_PLAY(FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py index a2b7772d26c5..e17a95fa38d0 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py @@ -143,8 +143,8 @@ class PolicyCfg(ObsGroup): #robot_pose=ObsTerm(func=mdp.robot_pose) #object_to_target = ObsTerm(func=mdp.position_command_error) #norm_error = ObsTerm(func=mdp.object_goal_norm_error) - # abs_joint_pos = ObsTerm(func=mdp.get_joint_pos) - #object_knocked = ObsTerm(func=mdp.object_knocked) + abs_joint_pos = ObsTerm(func=mdp.get_joint_pos) + object_knocked = ObsTerm(func=mdp.object_knocked) def __post_init__(self): self.enable_corruption = False self.concatenate_terms = False @@ -300,7 +300,7 @@ class TerminationsCfg: #joint_effort = DoneTerm(func=mdp.joint_effort_out_of_limit) object_dropping = DoneTerm( - func=mdp.root_height_below_minimum, params={"minimum_height": -0.05, "asset_cfg": SceneEntityCfg("object")} + func=mdp.root_height_below_minimum, params={"minimum_height": 0, "asset_cfg": SceneEntityCfg("object")} ) object_tipped = DoneTerm(func=mdp.object_knocked) #fix object dropping diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py index 162823fa1b64..7dce6fe950a8 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py @@ -161,14 +161,14 @@ def is_object_lifted( def ee_frame_pos(env: ManagerBasedRLEnv, ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame")) -> torch.Tensor: ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] ee_frame_pos = ee_frame.data.target_pos_w[:, 0, :] - env.scene.env_origins[:, 0:3] - + #print(f"EE pos : {ee_frame_pos}") return ee_frame_pos def ee_frame_quat(env: ManagerBasedRLEnv, ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame")) -> torch.Tensor: ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] ee_frame_quat = ee_frame.data.target_quat_w[:, 0, :] - + #print(f"EE pos : {ee_frame_quat}") return ee_frame_quat @@ -300,7 +300,7 @@ def object_tilt (env: ManagerBasedRLEnv, upper_object_cfg: SceneEntityCfg = Scen #print("obs term : tilt : ", tilt_deg) return tilt_deg -def object_knocked(env: ManagerBasedRLEnv, upper_object_cfg: SceneEntityCfg = SceneEntityCfg("object"), max_tilt: float = 90): +def object_knocked(env: ManagerBasedRLEnv, upper_object_cfg: SceneEntityCfg = SceneEntityCfg("object"), max_tilt: float = 45): object: RigidObject = env.scene[upper_object_cfg.name] tilt_deg = upright_tilt_deg(object.data.root_quat_w, object.data.default_root_state[:, 3:7]) if tilt_deg.cpu().detach().numpy()[0] > max_tilt: From 971732bb20fa0eac086060e9d8aa343e9907a6e7 Mon Sep 17 00:00:00 2001 From: LJones-alt Date: Fri, 31 Oct 2025 12:25:10 +0000 Subject: [PATCH 05/15] fixed the DiffIK Rel Controller --- .../robomimic/play_controller.py | 166 ++++++++---------- .../isaaclab/controllers/differential_ik.py | 4 +- 2 files changed, 80 insertions(+), 90 deletions(-) diff --git a/scripts/imitation_learning/robomimic/play_controller.py b/scripts/imitation_learning/robomimic/play_controller.py index 1246c87535ab..3d12adf65a82 100644 --- a/scripts/imitation_learning/robomimic/play_controller.py +++ b/scripts/imitation_learning/robomimic/play_controller.py @@ -101,42 +101,26 @@ def rollout_ensemble(env, success_term, horizon, device): """Perform a single rollout of the policy in the environment. - Args: policy: The robomimicpolicy to play. env: The environment to play in. horizon: The step horizon of each rollout. device: The device to run the policy on. - Returns: terminated: Whether the rollout terminated. traj: The trajectory of the rollout. """ - - - obs_dict, _ = env.reset() - #print(f"obs_dict type: {type(obs_dict)},\nobs_dict contents:\n {obs_dict}") - - traj = dict(actions=[], obs=[], next_obs=[], sub_obs=[], - uncertainties=[], min_actions=[], max_actions=[], - time_taken=[], joint_pos=[], recovery=[], failure=[], grasp=[], appr=[]) - + env.reset() ###### SETUP SWITCHING LOGIC #### sim = env.unwrapped.sim num_envs = env.num_envs - - certain_joint_positions = [] - ### SET USE RECOVERY TO FALSE #### - - # Set up recovery controller robot = env.unwrapped.scene["robot"] print(f"Unwrapped sim : {sim}") ee_recovery_pos = torch.tensor([[0.5206, 0.0096, 0.3751]], device=device) - ee_recovery_rot = torch.tensor([[ 0.6664, 0.0360, 0.7414, -0.0705]], device=device) # lets change this into the 6 element tensor they are expecting roll,pitch,yaw = euler_xyz_from_quat(ee_recovery_rot) @@ -146,97 +130,114 @@ def rollout_ensemble(env, success_term, horizon, device): ee_rpy = ee_rpy.unsqueeze(0) ee_goal = torch.cat([ee_recovery_pos, ee_rpy], dim=1) # Create controller - diff_ik_cfg = DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls") - diff_ik_controller = DifferentialIKController(diff_ik_cfg, num_envs=env.unwrapped.scene.num_envs, device=sim.device) - - + diff_ik_cfg = DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls") + #diff_ik_controller = DifferentialIKController(diff_ik_cfg, num_envs=env.unwrapped.scene.num_envs, device=sim.device) + diff_ik_controller = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKController(diff_ik_cfg, num_envs=env.unwrapped.scene.num_envs, device=sim.device), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + ) robot_entity_cfg = SceneEntityCfg("robot", joint_names=["panda_joint.*"], body_names=["panda_hand"]) robot_entity_cfg.resolve(env.unwrapped.scene) if robot.is_fixed_base: ee_jacobi_idx = robot_entity_cfg.body_ids[0] - 1 else: ee_jacobi_idx = robot_entity_cfg.body_ids[0] - + recovery=[] print(f'Running for horizon {horizon}') for i in range(horizon): + # collect kinematic state jacobian = robot.root_physx_view.get_jacobians()[:, ee_jacobi_idx, :, robot_entity_cfg.joint_ids] ee_pose_w = robot.data.body_pose_w[:, robot_entity_cfg.body_ids[0]] root_pose_w = robot.data.root_pose_w joint_pos = robot.data.joint_pos[:, robot_entity_cfg.joint_ids] - # current EE pose in base frame + # current EE pose in base/root frame ee_pos_b, ee_quat_b = subtract_frame_transforms( root_pose_w[:, 0:3], root_pose_w[:, 3:7], ee_pose_w[:, 0:3], ee_pose_w[:, 3:7] ) - print(f"ee_pos_b {ee_pos_b}, ee_quat_b : {ee_quat_b}") - # target EE pose (absolute, world frame → base frame) - target_pos_w = ee_recovery_pos # [1,3] - target_quat_w = ee_recovery_rot # [1,4] + # target EE pose in world -> convert to base/root frame + target_pos_w = ee_recovery_pos.to(device) + target_quat_w = ee_recovery_rot.to(device) target_pos_b, target_quat_b = subtract_frame_transforms( root_pose_w[:, 0:3], root_pose_w[:, 3:7], target_pos_w, target_quat_w ) - print(f"target_pos_b {target_pos_b}, target_quat_b {target_quat_b}") - - # --- compute relative pose command (6D) --- - # translation delta - delta_pos = target_pos_b - ee_pos_b # [1,3] - print(f"Delta pos {delta_pos}") - # rotation delta (ΔR = R_target * R_currentᵀ) - delta_rot = target_quat_b - ee_quat_b - print(f"Delat Rot {delta_rot}") - # convert ΔR to euler angles (XYZ convention) - r,p,y = euler_xyz_from_quat(delta_rot, wrap_to_2pi=False) - print(f"[DEBUG] Delat R {r}, P {p}, Y {y}") - delta_rpy = torch.cat([r,p,y], dim=-1) - delta_rpy = torch.unsqueeze(delta_rpy,0) - print(f"[DEBUG] RPY {delta_rpy}") - # --- build relative command --- - ee_goal = torch.cat([delta_pos, delta_rpy], dim=-1) # [1,6] - - # debug info + # --- Translation delta (absolute -> small step) --- + delta_pos_abs = target_pos_b - ee_pos_b # full error [N,3] + # convert to small incremental command + pos_gain = 0.1 # tune: fraction of error to move per step + delta_pos = pos_gain * delta_pos_abs + # clamp translation step (meters per step) + clamp = 0.01 + delta_pos = torch.clamp(delta_pos, min=-clamp, max=clamp) + + # --- Rotation delta (correct quaternion math) --- + # compute conjugate of current quaternion (w, -x, -y, -z) + q_cur_conj = torch.cat([ee_quat_b[:, :1], -ee_quat_b[:, 1:]], dim=-1) + # compute relative quaternion q_rel = q_target * q_cur_conj + delta_q = quat_mul(target_quat_b, q_cur_conj) # uses imported quat_mul + # convert delta quaternion to RPY (XYZ extrinsic) + r, p, y = euler_xyz_from_quat(delta_q, wrap_to_2pi=False) # each shape (N,) + delta_rpy = torch.stack([r, p, y], dim=-1) # [N,3] + + # optionally scale rotation step (so rotations are not huge) + rot_gain = 1.0 # keep 1.0 if delta_q already represents relative rotation for controller + delta_rpy = rot_gain * delta_rpy + # clamp rotation step (radians per step) + delta_rpy = torch.clamp(delta_rpy, min=-0.5, max=0.5) + + # --- build controller command: [dx,dy,dz, droll,dpitch,dyaw] --- + ee_goal = torch.cat([delta_pos, delta_rpy], dim=-1) # [N,6] + + # Debug prints (inspect magnitudes) + print(f"[DEBUG] ee_pos_b: {ee_pos_b}") + print(f"[DEBUG] target_pos_b: {target_pos_b}") + print(f"[DEBUG] delta_pos_abs norm: {torch.norm(delta_pos_abs).item():.4f}") + print(f"[DEBUG] delta_pos (sent): {delta_pos}") + print(f"[DEBUG] delta_rpy (sent, rad): {delta_rpy}") print(f"[DEBUG] ee_goal (Δx,Δy,Δz,Δr,Δp,Δy): {ee_goal}") - print(f"[DEBUG] Current pos_b: {ee_pos_b}") - print(f"[DEBUG] Target pos_b: {target_pos_b}") - # --- send command to controller --- - diff_ik_controller.set_command(ee_goal, ee_pos_b, ee_quat_b) - - # compute joint targets - joint_pos_des = diff_ik_controller.compute(ee_pos_b, ee_quat_b, jacobian, joint_pos) - - # (optional) denormalize actions if necessary - if args_cli.norm_factor_min is not None and args_cli.norm_factor_max is not None: - actions = ((actions + 1) * (args_cli.norm_factor_max - args_cli.norm_factor_min)) / 2 + args_cli.norm_factor_min - - # --- execute recovery movement --- - pos_err = torch.norm(target_pos_b - ee_pos_b) - actions=joint_pos_des - metrics = ensemble_uncertainty(ensemble, obs) - metrics['min']=actions - metrics['max']=actions - uncertainty = 0 - - if pos_err > 1e-3: - robot.set_joint_position_target(joint_pos_des, joint_ids=robot_entity_cfg.joint_ids) - env.scene.write_data_to_sim() - obs_dict, _, terminated, truncated, _ = env.step(joint_pos_des) - else: - print(f"[Recovery] Target reached — ending recovery mode.") - recovery_mode = False - recovery_cooldown_active = True - print("[Recovery] Cooldown Activated.") + # --- set command on the existing controller (action cfg wraps controller) --- + # your diff_ik_action_cfg holds the controller at .controller + diff_ctrl = diff_ik_controller.controller if hasattr(diff_ik_controller, "controller") else diff_ik_controller + # ensure correct shape + assert ee_goal.dim() == 2 and ee_goal.shape[1] == diff_ctrl._command.shape[1], \ + f"Mismatch: ee_goal.shape={ee_goal.shape}, controller expects {diff_ctrl._command.shape}" + # set command and compute joint targets + diff_ctrl.set_command(ee_goal, ee_pos_b, ee_quat_b) + joint_pos_des = diff_ctrl.compute(ee_pos_b, ee_quat_b, jacobian, joint_pos) + # --- apply joint targets via articulation API (do NOT call env.step(joint_pos_des)) --- + robot.set_joint_position_target(joint_pos_des, joint_ids=robot_entity_cfg.joint_ids) + # advance the sim (two options - use direct sim step or call env.step with zero action) + env.scene.write_data_to_sim() + env.sim.step() + # If you need the gym env to also advance and return next obs, call env.step with a zero action: + zero_action = torch.zeros(1, env.action_space.shape[1], device=device) + obs_dict, _, terminated, truncated, _ = env.step(zero_action) - + # check if we reached target (use absolute pos error) + pos_err = torch.norm(target_pos_b - ee_pos_b) + print(f"[DEBUG] post-step pos_err (before update): {pos_err.item():.6f}") + if pos_err <= 1e-3: + print("[Recovery] Target reached — ending recovery mode.") + recovery_mode = False + recovery_cooldown_active = True + print("[Recovery] Cooldown Activated.") + + def main(): """Run a trained policy from robomimic with Isaac Lab environment.""" # parse configuration @@ -244,15 +245,10 @@ def main(): # Set observations to dictionary mode for Robomimic env_cfg.observations.policy.concatenate_terms = False - #create a log handler # loghelper = LoggingHelper() - - - # Disable recorder env_cfg.recorders = None - # Extract success checking function success_term = env_cfg.terminations.success env_cfg.terminations.success = None @@ -260,26 +256,20 @@ def main(): # Get timeout signal timeout = env_cfg.terminations.time_out #env_cfg.terminations.time_out = None - # Create environment env = gym.make(args_cli.task, cfg=env_cfg).unwrapped - # Set seed9 torch.manual_seed(args_cli.seed) env.seed(args_cli.seed) - # Acquire device device = TorchUtils.get_torch_device(try_to_use_cuda=True) - for trial in range(args_cli.num_rollouts): print(f"[INFO] Starting trial {trial}") - terminated, traj, recovery_activated_during_rollout = rollout_ensemble( env, success_term, args_cli.horizon, device) + rollout_ensemble( env, success_term, args_cli.horizon, device) # save the uncertainties env.close() - - if __name__ == "__main__": # run the main function main() diff --git a/source/isaaclab/isaaclab/controllers/differential_ik.py b/source/isaaclab/isaaclab/controllers/differential_ik.py index 0a9d77d4216b..e10ff6d9cbde 100644 --- a/source/isaaclab/isaaclab/controllers/differential_ik.py +++ b/source/isaaclab/isaaclab/controllers/differential_ik.py @@ -117,7 +117,7 @@ def set_command( ValueError: If the command type is ``pose_rel`` and either :attr:`ee_pos` or :attr:`ee_quat` is None. """ # store command - print(f"[DEBUG] command : {command}") + #print(f"[DEBUG] command : {command}") self._command[:] = command # compute the desired end-effector pose @@ -143,7 +143,7 @@ def set_command( "Neither end-effector position nor orientation can be None for `pose_rel` command type!" ) self.ee_pos_des, self.ee_quat_des = apply_delta_pose(ee_pos, ee_quat, self._command) - print(f"Calculated pos and quat {self.ee_pos_des}, {self.ee_quat_des}") + # print(f"Calculated pos and quat {self.ee_pos_des}, {self.ee_quat_des}") else: self.ee_pos_des = self._command[:, 0:3] self.ee_quat_des = self._command[:, 3:7] From 6d8cb1947e0968cf50376d731e3d7884705cd726 Mon Sep 17 00:00:00 2001 From: LJones-alt Date: Mon, 10 Nov 2025 13:25:54 +0000 Subject: [PATCH 06/15] with backup controller! --- docker/docker-compose.yaml | 8 +- .../robomimic/backup_controller.py | 410 ++++++++++++++++++ .../robomimic/backup_controller_handler.py | 95 ++++ .../robomimic/play_controller.py | 122 ++++-- .../robomimic/play_ensemble.py | 314 ++++---------- .../robomimic/play_with_backup.py | 178 ++++++++ .../isaaclab/controllers/differential_ik.py | 2 +- .../envs/mdp/actions/binary_joint_actions.py | 1 + .../isaaclab/safety/switchingLogic.py | 9 +- .../manipulation/cube_lift/lift_env_cfg.py | 3 +- .../cube_lift/mdp/observations.py | 2 +- 11 files changed, 874 insertions(+), 270 deletions(-) create mode 100644 scripts/imitation_learning/robomimic/backup_controller.py create mode 100644 scripts/imitation_learning/robomimic/backup_controller_handler.py create mode 100644 scripts/imitation_learning/robomimic/play_with_backup.py diff --git a/docker/docker-compose.yaml b/docker/docker-compose.yaml index f1cf86c2daa9..1f14102eced6 100644 --- a/docker/docker-compose.yaml +++ b/docker/docker-compose.yaml @@ -112,8 +112,8 @@ services: tty: true devices: - /dev/hidraw0:/dev/hidraw0 - - /dev/hidraw1:/dev/hidraw1 - - /dev/hidraw2:/dev/hidraw2 + # - /dev/hidraw1:/dev/hidraw1 + # - /dev/hidraw2:/dev/hidraw2 # This service adds a ROS2 Humble # installation on top of the base image @@ -145,8 +145,8 @@ services: tty: true devices: - /dev/hidraw0:/dev/hidraw0 - - /dev/hidraw1:/dev/hidraw1 - - /dev/hidraw2:/dev/hidraw2 + # - /dev/hidraw1:/dev/hidraw1 + # - /dev/hidraw2:/dev/hidraw2 volumes: # isaac-sim diff --git a/scripts/imitation_learning/robomimic/backup_controller.py b/scripts/imitation_learning/robomimic/backup_controller.py new file mode 100644 index 000000000000..f7952aebe822 --- /dev/null +++ b/scripts/imitation_learning/robomimic/backup_controller.py @@ -0,0 +1,410 @@ +import warp as wp +import gymnasium as gym +import torch +from collections.abc import Sequence +import numpy as np + +# this is a backup state machine controller +# initialize warp +wp.init() + + +class GripperState: + """States for the gripper.""" + + OPEN = wp.constant(1.0) + CLOSE = wp.constant(-1.0) + +class BackupSMWaitTime: + """Additional wait times (in s) for states for before switching.""" + + REST = wp.constant(0.5) + APPROACH_ABOVE_OBJECT = wp.constant(0.1) + APPROACH_OBJECT = wp.constant(0.1) + GRASP_OBJECT = wp.constant(0.1) + LIFT_OBJECT = wp.constant(0.1) + MIDPOINT = wp.constant(0.1) + APPROACH_ABOVE_GOAL = wp.constant(0.1) + APPROACH_GOAL = wp.constant(6) + +class BackupSM: + """States for the pick state machine.""" + + REST = wp.constant(0) + APPROACH_ABOVE_OBJECT = wp.constant(1) + APPROACH_OBJECT = wp.constant(2) + GRASP_OBJECT = wp.constant(3) + LIFT_OBJECT = wp.constant(4) + MIDPOINT = wp.constant(5) + APPROACH_ABOVE_GOAL = wp.constant(6) + APPROACH_GOAL = wp.constant(7) + #UNGRASP_OBJECT = wp.constant(7) + +@wp.func +def distance_below_threshold(current_pos: wp.vec3, desired_pos: wp.vec3, threshold: float) -> bool: + return wp.length(current_pos - desired_pos) < threshold + + +@wp.kernel +def infer_state_machine( + dt: wp.array(dtype=float), + sm_state: wp.array(dtype=int), + sm_wait_time: wp.array(dtype=float), + ee_pose: wp.array(dtype=wp.transform), # current ee pose + object_pose: wp.array(dtype=wp.transform), # start object location + current_object_pose: wp.array(dtype=wp.transform), # current object pose + final_object_pose: wp.array(dtype=wp.transform), + rest_ee_pose : wp.array(dtype=wp.transform), # rest position + des_ee_pose: wp.array(dtype=wp.transform), # what we return + gripper_state: wp.array(dtype=float), + offset: wp.array(dtype=wp.transform), + position_threshold: float, + debug_des_pose: wp.array(dtype=wp.transform), # NEW + debug_cur_pose: wp.array(dtype=wp.transform), # NEW + debug_state: wp.array(dtype=int), # New +): + # retrieve thread id + tid = wp.tid() + # retrieve state machine state + state = sm_state[tid] + # decide next state + if state == BackupSM.REST: + # go back to reset position + des_ee_pose[tid] = rest_ee_pose[tid] + gripper_state[tid] = GripperState.CLOSE + # print("\n[SM] REST") + # wait for a while + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + 0.02, + ): + if sm_wait_time[tid] >= BackupSMWaitTime.REST: + print("[SM_INFO] : Moving from REST to APPROACH_ABOVE_OBJECT") + # move to next state and reset wait time + sm_state[tid] = BackupSM.APPROACH_ABOVE_OBJECT + sm_wait_time[tid] = 0.0 + elif state == BackupSM.APPROACH_ABOVE_OBJECT: + # print("[SM] APPR above obj") + pose_pos = wp.transform_get_translation(current_object_pose[tid]) + pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + + des_ee_pose[tid] = wp.transform(pose_pos, pose_rot) + des_ee_pose[tid] = wp.transform_multiply(offset[tid], des_ee_pose[tid]) + gripper_state[tid] = GripperState.CLOSE + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + position_threshold, + ): + # wait for a while + if sm_wait_time[tid] >= BackupSMWaitTime.APPROACH_ABOVE_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from APPR_ABOVE to APPROACH_OBJECT") + sm_state[tid] = BackupSM.APPROACH_OBJECT + sm_wait_time[tid] = 0.0 + elif state == BackupSM.APPROACH_OBJECT: + # print("[SM] approach object") + + pose_pos = wp.transform_get_translation(current_object_pose[tid]) + pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + # Apply offset in x-direction (5 cm = 0.05 m) + offset_pos = wp.vec3(pose_pos.x -0.1, pose_pos.y+0.02, pose_pos.z+0.02) #sample vial + 0.04 + des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) + gripper_state[tid] = GripperState.OPEN + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + position_threshold, + ): + if sm_wait_time[tid] >= BackupSMWaitTime.APPROACH_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from APPR_OBJ to GRASP_OBJECT") + sm_state[tid] = BackupSM.GRASP_OBJECT + sm_wait_time[tid] = 0.0 + elif state == BackupSM.GRASP_OBJECT: + # print("[SM] GRASP") + pose_pos = wp.transform_get_translation(current_object_pose[tid]) + pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + # Apply offset in x-direction (5 cm = 0.05 m) + offset_pos = wp.vec3() #sample vial + 0.04 + des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if sm_wait_time[tid] >= BackupSMWaitTime.GRASP_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from GRSP to LIFT_OBJECT") + sm_state[tid] = BackupSM.LIFT_OBJECT + sm_wait_time[tid] = 0.0 + elif state == BackupSM.LIFT_OBJECT: + #print("[SM] LIFT") # uses starting position + pose_pos = wp.transform_get_translation(object_pose[tid]) + pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + # Apply offset in x-direction (5 cm = 0.05 m) + offset_pos = wp.vec3(pose_pos.x -0.1, pose_pos.y+0.02, pose_pos.z+0.2) #sample vial + 0.04 + des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + 0.03, + ): + # wait for a while + if sm_wait_time[tid] >= BackupSMWaitTime.LIFT_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from LIFT to MIDPOINT") + sm_state[tid] = BackupSM.MIDPOINT + sm_wait_time[tid] = 0.0 + elif state == BackupSM.MIDPOINT: + #print("[SM] MIDPOINT") + pose_pos = wp.transform_get_translation(rest_ee_pose[tid]) + pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + # Apply offset in x-direction (5 cm = 0.05 m) + #offset_pos = wp.vec3(pose_pos.x -0.1, pose_pos.y+0.02, pose_pos.z+0.2) #sample vial + 0.04 + des_ee_pose[tid] = rest_ee_pose[tid] + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + 0.03, + ): + # wait for a while + if sm_wait_time[tid] >= BackupSMWaitTime.LIFT_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from MID to APPR goal") + sm_state[tid] = BackupSM.APPROACH_ABOVE_GOAL + sm_wait_time[tid] = 0.0 + elif state == BackupSM.APPROACH_ABOVE_GOAL: + #print("[SM] Approach above goal") + offset_pos = wp.transform_get_translation(offset[tid]) + offset_rot = wp.transform_get_rotation(offset[tid]) + offset_pos = wp.vec3(offset_pos.x, offset_pos.y, offset_pos.z) # raise 25 cm + new_offset = wp.transform(offset_pos, offset_rot) + above_target_pose = wp.transform_multiply(new_offset, final_object_pose[tid]) + # Blend time for smooth approach + APPROACH_BLEND_TIME = 0.4 # seconds, tune as needed + alpha = wp.clamp(sm_wait_time[tid] / APPROACH_BLEND_TIME, 0.0, 1.0) + # Interpolate position and rotation + current_pos = wp.transform_get_translation(ee_pose[tid]) + current_rot = wp.transform_get_rotation(ee_pose[tid]) + + target_pos = wp.transform_get_translation(above_target_pose) + target_rot = wp.transform_get_rotation(above_target_pose) + + pos_interp = wp.lerp(current_pos, target_pos, alpha) + rot_interp = wp.quat_slerp(current_rot, target_rot, alpha) + # Set interpolated pose + des_ee_pose[tid] = wp.transform(pos_interp, rot_interp) + gripper_state[tid] = GripperState.CLOSE + # Evaluate readiness to transition + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + 0.02): + if sm_wait_time[tid] >= BackupSMWaitTime.APPROACH_ABOVE_GOAL: + print("[SM_INFO] : Moving from APPR_ABOVE to APPROACH_GOAL") + sm_state[tid] = BackupSM.APPROACH_GOAL + sm_wait_time[tid] = 0.0 + elif state == BackupSM.APPROACH_GOAL: + #print("[SM] approach goal") + # print("[SM_INFO] : in state ungrasp") + pose_pos = wp.transform_get_translation(final_object_pose[tid]) + pose_rot = wp.transform_get_rotation(final_object_pose[tid]) + # Apply offset in x-direction (5 cm = 0.05 m) + offset_pos = wp.vec3(pose_pos.x, pose_pos.y+0.1, pose_pos.z+0.02) + des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + 0.01, + ): + if sm_wait_time[tid] >= BackupSMWaitTime.APPROACH_GOAL: + # move to next state and reset wait time + print("[SM_INFO] : Moving from apprach goal to rest") + sm_state[tid] = BackupSM.REST + sm_wait_time[tid] = 0.0 + + + # increment wait time + sm_wait_time[tid] = sm_wait_time[tid] + dt[tid] + debug_des_pose[tid] = des_ee_pose[tid] + debug_cur_pose[tid] = ee_pose[tid] + debug_state[tid] = sm_state[tid] + + + +class BackupControllerSM: + def __init__(self, dt: float, num_envs: int, device: torch.device | str = "cpu", position_threshold=0.02, offset = torch.tensor([[-0.1, 0, 0.1]], device='cuda:0')): + """Initialize the state machine. + + Args: + dt: The environment time step. + num_envs: The number of environments to simulate. + device: The device to run the state machine on. + """ + ## debug + + print(f'dt : {dt},num_envs {num_envs}, device {device}, pos_thresh {position_threshold} ') + # save parameters + self.dt = float(dt) + self.num_envs = num_envs + self.device = device + self.position_threshold = position_threshold + + # initialize state machine + self.sm_dt = torch.full((self.num_envs,), self.dt, device=self.device) + self.sm_state = torch.full((self.num_envs,), 0, dtype=torch.int32, device=self.device) + self.sm_wait_time = torch.zeros((self.num_envs,), device=self.device) + + # desired state + self.des_ee_pose = torch.zeros((self.num_envs, 7), device=self.device) + self.des_gripper_state = torch.full((self.num_envs,), 0.0, device=self.device) + self.final_object_pose = torch.zeros((self.num_envs, 7), device=self.device) + self.rest_pose = torch.zeros((self.num_envs, 7), device=self.device) + # approach above object offset + + self.offset = torch.zeros((self.num_envs, 7), device=self.device) + self.offset[:, 2] = 0.2 + self.offset[:,0] = -0.1 + self.offset[:, -1] = 1.0 # warp expects quaternion as (x, y, z, w) + + # convert to warp + self.sm_dt_wp = wp.from_torch(self.sm_dt, wp.float32) + self.sm_state_wp = wp.from_torch(self.sm_state, wp.int32) + self.sm_wait_time_wp = wp.from_torch(self.sm_wait_time, wp.float32) + self.des_ee_pose_wp = wp.from_torch(self.des_ee_pose, wp.transform) + self.des_gripper_state_wp = wp.from_torch(self.des_gripper_state, wp.float32) + self.final_object_pose_wp = wp.from_torch(self.final_object_pose, wp.transform) + self.rest_pose_wp = wp.from_torch(self.rest_pose, wp.transform) + self.offset_wp = wp.from_torch(self.offset, wp.transform) + ## For Debug + self.debug_des_pose = torch.zeros((self.num_envs, 7), device=self.device) + self.debug_cur_pose = torch.zeros((self.num_envs, 7), device=self.device) + self.debug_state = torch.full((self.num_envs,), 0, dtype=torch.int32, device=self.device) + + self.debug_des_pose_wp = wp.from_torch(self.debug_des_pose, wp.transform) + self.debug_cur_pose_wp = wp.from_torch(self.debug_cur_pose, wp.transform) + self.debug_state_wp = wp.from_torch(self.debug_state, wp.int32) + + def reset_idx(self, env_ids: Sequence[int] = None): + """Reset the state machine.""" + print(f'[SM RESET]') + if env_ids is None: + env_ids = slice(None) + self.sm_state[env_ids] = 0 + self.sm_wait_time[env_ids] = 0.0 + + def _ensure_7d(self,tensor: torch.Tensor, device): + # accepts (N,3) -> returns (N,7) with identity quaternion + if tensor.ndim != 2: + raise ValueError(f"expected 2D tensor, got shape {tensor.shape}") + if tensor.shape[1] == 7: + return tensor + if tensor.shape[1] == 3: + quat = torch.tensor([0.0,0.0,0.0,1.0], device=device, dtype=tensor.dtype) + quat = quat.unsqueeze(0).expand(tensor.shape[0], -1) + return torch.cat([tensor, quat], dim=-1) + raise ValueError(f"pose must have 3 or 7 elements, got {tensor.shape[1]}") + + + def compute(self, ee_pose: torch.Tensor, start_object_pose: torch.Tensor, current_object_pose: torch.Tensor, final_object_pose: torch.Tensor, rest_pose: torch.Tensor, sm_state: int =0, env_num = 0) -> torch.Tensor: + """Compute the desired state of the robot's end-effector and the gripper.""" + # convert all transformations from (w, x, y, z) to (x, y, z, w) + # print(f'[SM DEBUG] Doing State Comp') + # print(f'[SM DEBUG] ee_pose {ee_pose}') + # print(f'[SM DEBUG] object pose {object_pose}') + # print(f'[SM DEBUG] desired pose {des_object_pose}') + # print(f'[SM DEBUG] final pose {final_object_pose}') + # print(f'[SM DEBUG] rest pose {rest_pose}') + # before reordering + # print("\n[BackupControllerSM::compute] called with inputs:") + # print(f" sm_state: {sm_state}, env_num: {env_num}") + + # def short_tensor(t, name): + # if t is None: + # print(f" {name}: None") + # elif not isinstance(t, torch.Tensor): + # print(f" {name}: {t} (type={type(t)})") + # else: + # shape = tuple(t.shape) + # device = t.device + # dtype = t.dtype + # # show first few elements flattened + # vals = t.flatten()[:6].detach().cpu().numpy() + # print(f" {name}: shape={shape}, device={device}, dtype={dtype}, values={vals}") + + # short_tensor(ee_pose, "ee_pose") + # short_tensor(object_pose, "object_pose") + # short_tensor(des_object_pose, "des_object_pose") + # short_tensor(final_object_pose, "final_object_pose") + # short_tensor(rest_pose, "rest_pose") + # print("-" * 80) + # print(f"Got given state {sm_state}") + current_object_pose = self._ensure_7d(current_object_pose, self.device) + final_object_pose = self._ensure_7d(final_object_pose, self.device) + ee_pose = self._ensure_7d(ee_pose, self.device) + start_object_pose = self._ensure_7d(start_object_pose, self.device) + rest_pose = self._ensure_7d(rest_pose, self.device) + # now safe to reorder + ee_pose = ee_pose[:, [0, 1, 2, 4, 5, 6, 3]] + start_object_pose = start_object_pose[:, [0, 1, 2, 4, 5, 6, 3]] + current_object_pose = current_object_pose[:, [0, 1, 2, 4, 5, 6, 3]] + final_object_pose = final_object_pose[:, [0, 1, 2, 4, 5, 6, 3]] + rest_pose = rest_pose[:, [0, 1, 2, 4, 5, 6, 3]] + # convert to warp + ee_pose_wp = wp.from_torch(ee_pose.contiguous(), wp.transform) + start_object_pose_wp = wp.from_torch(start_object_pose.contiguous(), wp.transform) + current_object_pose_wp = wp.from_torch(current_object_pose.contiguous(), wp.transform) + final_object_pose_wp = wp.from_torch(final_object_pose.contiguous(), wp.transform) + rest_pose_wp = wp.from_torch(rest_pose.contiguous(), wp.transform) + # run state machine + # get state from env - convert to warp again + self.sm_state[env_num] = sm_state + self.sm_state_wp = wp.from_torch(self.sm_state, wp.int32) + # launch + wp.launch( + kernel=infer_state_machine, + dim=self.num_envs, + inputs=[ + self.sm_dt_wp, + self.sm_state_wp, + self.sm_wait_time_wp, + ee_pose_wp, + start_object_pose_wp, + current_object_pose_wp, + final_object_pose_wp, + rest_pose_wp, + self.des_ee_pose_wp, + self.des_gripper_state_wp, + self.offset_wp, + self.position_threshold, + self.debug_des_pose_wp, + self.debug_cur_pose_wp, + self.debug_state_wp, + ], + device=self.device, + ) + des_debug = self.debug_des_pose.detach().cpu().numpy() + cur_debug = self.debug_cur_pose.detach().cpu().numpy() + debug_state = self.debug_state.detach().cpu().numpy() + for i in range(self.num_envs): + des_pos, des_quat = des_debug[i][:3], des_debug[i][3:] + cur_pos, cur_quat = cur_debug[i][:3], cur_debug[i][3:] + # compute Euclidean distance with numpy + dist = np.linalg.norm(cur_pos - des_pos) + # print(f"StateMachine debug state {debug_state}") + # print(f"[Env {i}]") + # print(f" Current -> pos: {cur_pos}, quat: {cur_quat}") + # print(f" Desired -> pos: {des_pos}, quat: {des_quat}") + #print(f" Euclidean distance: {dist:.4f}") + #print(f"returned state : {self.sm_state}") + # convert transformations back to (w, x, y, z) + des_ee_pose = self.des_ee_pose[:, [0, 1, 2, 6, 3, 4, 5]] + # convert to torch + #print(f'[SM DEBUG] new desired poition : {des_ee_pose}, gripper state {self.des_gripper_state}') + return des_ee_pose , self.des_gripper_state, self.sm_state.clone().item() + #return torch.cat([des_ee_pose, self.des_gripper_state.unsqueeze(-1)], dim=-1) diff --git a/scripts/imitation_learning/robomimic/backup_controller_handler.py b/scripts/imitation_learning/robomimic/backup_controller_handler.py new file mode 100644 index 000000000000..261fc906396a --- /dev/null +++ b/scripts/imitation_learning/robomimic/backup_controller_handler.py @@ -0,0 +1,95 @@ +from isaaclab.controllers import DifferentialIKController, DifferentialIKControllerCfg +from isaaclab.utils.math import subtract_frame_transforms, euler_xyz_from_quat, quat_mul +from isaaclab.managers import SceneEntityCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg, BinaryJointPositionActionCfg +from isaaclab.envs.mdp.actions.binary_joint_actions import BinaryJointPositionAction +from backup_controller import BackupControllerSM +import torch + + +class BackupController: + def __init__(self,env, device): + self.env = env + self.sim = env.unwrapped.sim + self.num_envs = env.num_envs + self.device = device + self.rest_pos = self._get_rest_pos() + self.backup_controller = BackupControllerSM(0.01*2, self.num_envs,'cuda' , 0.02) + self.robot = env.unwrapped.scene["robot"] + self.robot_entity_cfg , self.ee_jacobi_idx = self._setup_robot() + self.state_guess = 0 + self.pos_gain = 0.5 + self.clamp = 0.05 + self.rot_gain = 1.0 + self.rot_clamp = 0.5 + self.object_start_pose = self._get_start_pos() + + self.reset() + + def reset(self): + self.backup_controller.reset_idx() + + def _get_start_pos(self): + object_pose_w = self.env.unwrapped.scene["object"].data.root_pose_w + return object_pose_w + + def _get_rest_pos(self): + rest_pos = torch.tensor([[0.5206, 0.0096, 0.3751]], device=self.device) + ee_recovery_rot = torch.tensor([[ 0.6664, 0.0360, 0.7414, -0.0705]], device=self.device) + print(f"recovery rot : {ee_recovery_rot}") + # lets change this into the 6 element tensor they are expecting + roll,pitch,yaw = euler_xyz_from_quat(ee_recovery_rot) + rest_pos = torch.cat([rest_pos, ee_recovery_rot], dim =-1) + return rest_pos + + def _setup_robot(self): + robot_entity_cfg = SceneEntityCfg("robot", joint_names=["panda_joint.*"], body_names=["panda_hand"]) + robot_entity_cfg.resolve(self.env.unwrapped.scene) + if self.robot.is_fixed_base: + ee_jacobi_idx = robot_entity_cfg.body_ids[0] - 1 + else: + ee_jacobi_idx = robot_entity_cfg.body_ids[0] + return robot_entity_cfg, ee_jacobi_idx + + def get_controller_action(self, state_guess, env_num): + + current_object_pose = self.env.unwrapped.scene["object"].data.root_pose_w + root_pose_w = self.robot.data.root_pose_w + ee_pose_w = self.robot.data.body_pose_w[:, self.robot_entity_cfg.body_ids[0]] + # current EE pose in base/root frame + ee_pos_b, ee_quat_b = subtract_frame_transforms( + root_pose_w[:, 0:3], root_pose_w[:, 3:7], + ee_pose_w[:, 0:3], ee_pose_w[:, 3:7] + ) + object_pose = self.env.unwrapped.command_manager.get_command("object_pose") + ee_recovery, gripper , state_guess= self.backup_controller.compute(ee_pose =ee_pose_w, start_object_pose = self.object_start_pose, current_object_pose=current_object_pose, final_object_pose=object_pose, rest_pose=self.rest_pos, sm_state=state_guess) + #print(f"[BAKCUP] state {state_guess}") + + #return ee_recovery , gripper , state_guess + ee_recovery_pos, ee_recovery_rot = ee_recovery[:,0:3], ee_recovery[:,3:7] + target_pos_w = ee_recovery_pos.to(self.device) + target_quat_w = ee_recovery_rot.to(self.device) + target_pos_b, target_quat_b = subtract_frame_transforms( + root_pose_w[:, 0:3], root_pose_w[:, 3:7], + target_pos_w, target_quat_w + ) + ## calc the delta pos action + delta_pos_abs = target_pos_b - ee_pos_b + delta_pos = self.pos_gain * delta_pos_abs + delta_pos = torch.clamp(delta_pos, min=-self.clamp, max=self.clamp) + + ## calc the delta rot action + # compute conjugate of current quaternion (w, -x, -y, -z) + q_cur_conj = torch.cat([ee_quat_b[:, :1], -ee_quat_b[:, 1:]], dim=-1) + # compute relative quaternion q_rel = q_target * q_cur_conj + delta_q = quat_mul(target_quat_b, q_cur_conj) # uses imported quat_mul + # convert delta quaternion to RPY (XYZ extrinsic) + r, p, y = euler_xyz_from_quat(delta_q, wrap_to_2pi=False) # each shape (N,) + delta_rpy = torch.stack([r, p, y], dim=-1) # [N,3] + delta_rpy = self.rot_gain * delta_rpy + delta_rpy = torch.clamp(delta_rpy, min=-self.rot_clamp, max=self.rot_clamp) + # --- build controller command: [dx,dy,dz, droll,dpitch,dyaw] --- + ee_goal = torch.cat([delta_pos, delta_rpy], dim=-1) # [N,6] + + action=torch.cat([ee_goal, gripper.unsqueeze(0)], dim=-1) + return action, state_guess \ No newline at end of file diff --git a/scripts/imitation_learning/robomimic/play_controller.py b/scripts/imitation_learning/robomimic/play_controller.py index 3d12adf65a82..166b1d518540 100644 --- a/scripts/imitation_learning/robomimic/play_controller.py +++ b/scripts/imitation_learning/robomimic/play_controller.py @@ -94,10 +94,10 @@ from isaaclab.controllers import DifferentialIKController, DifferentialIKControllerCfg from isaaclab.utils.math import subtract_frame_transforms, euler_xyz_from_quat from isaaclab.managers import SceneEntityCfg -from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg, BinaryJointPositionActionCfg +from isaaclab.envs.mdp.actions.binary_joint_actions import BinaryJointPositionAction +from backup_controller import * -#import chills.tasks -#from chills.tasks.mdp import upright_tilt_deg def rollout_ensemble(env, success_term, horizon, device): """Perform a single rollout of the policy in the environment. @@ -118,14 +118,27 @@ def rollout_ensemble(env, success_term, horizon, device): ### SET USE RECOVERY TO FALSE #### # Set up recovery controller + backup_controller = BackupControllerSM(0.01*2, num_envs,'cuda' , 0.01) + backup_controller.reset_idx() # none so reset all + robot = env.unwrapped.scene["robot"] - print(f"Unwrapped sim : {sim}") - ee_recovery_pos = torch.tensor([[0.5206, 0.0096, 0.3751]], device=device) + print(f"Robot : {robot}") + # print(f"Unwrapped sim : {sim}") + object_pose_w = env.unwrapped.scene["object"].data.root_pose_w + print(f"object pose : {object_pose_w}") + ee_recovery_pos = object_pose_w[:,0:3] + offset = torch.tensor([[-0.1, 0, 0.1]], device='cuda:0') + ee_recovery_pos_offset = ee_recovery_pos + offset + + print(f"recovery pos : {ee_recovery_pos_offset}") + ee_recovery_pos = ee_recovery_pos_offset + rest_pos = torch.tensor([[0.5206, 0.0096, 0.3751]], device=device) ee_recovery_rot = torch.tensor([[ 0.6664, 0.0360, 0.7414, -0.0705]], device=device) + print(f"recovery rot : {ee_recovery_rot}") # lets change this into the 6 element tensor they are expecting roll,pitch,yaw = euler_xyz_from_quat(ee_recovery_rot) - ee_recovery_goal = torch.cat([ee_recovery_pos, ee_recovery_rot], dim =-1) - print(f"I got this roll {roll}, pitch {pitch}, yaw {yaw} and full tensor {torch.cat([roll, pitch, yaw], dim=-1)}\n i need to add with {ee_recovery_pos}") + rest_pos = torch.cat([rest_pos, ee_recovery_rot], dim =-1) + # print(f"I got this roll {roll}, pitch {pitch}, yaw {yaw} and full tensor {torch.cat([roll, pitch, yaw], dim=-1)}\n i need to add with {ee_recovery_pos}") ee_rpy = torch.cat([roll, pitch, yaw], dim=-1) ee_rpy = ee_rpy.unsqueeze(0) ee_goal = torch.cat([ee_recovery_pos, ee_rpy], dim=1) @@ -142,16 +155,34 @@ def rollout_ensemble(env, success_term, horizon, device): ) robot_entity_cfg = SceneEntityCfg("robot", joint_names=["panda_joint.*"], body_names=["panda_hand"]) robot_entity_cfg.resolve(env.unwrapped.scene) + gripper_entity_cfg = SceneEntityCfg("robot", joint_names=["panda_finger.*"], body_names=["panda_hand"]) + gripper_entity_cfg.resolve(env.unwrapped.scene) + print(f"Found this gripper : {gripper_entity_cfg}") if robot.is_fixed_base: ee_jacobi_idx = robot_entity_cfg.body_ids[0] - 1 else: ee_jacobi_idx = robot_entity_cfg.body_ids[0] recovery=[] - print(f'Running for horizon {horizon}') + + ## gripper control + gripper_control_cfg = BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["panda_finger.*"], + open_command_expr={"panda_finger_.*": 0.04}, + close_command_expr={"panda_finger_.*": 0.0}, + ) + gripper_controller = BinaryJointPositionAction(cfg=gripper_control_cfg, env=env) + # print(f'Running for horizon {horizon}') + state_guess = 2 + for i in range(horizon): # collect kinematic state + + ## use the State Machine to get the goal position + jacobian = robot.root_physx_view.get_jacobians()[:, ee_jacobi_idx, :, robot_entity_cfg.joint_ids] ee_pose_w = robot.data.body_pose_w[:, robot_entity_cfg.body_ids[0]] + #print(f"EE pos {ee_pose_w}") root_pose_w = robot.data.root_pose_w joint_pos = robot.data.joint_pos[:, robot_entity_cfg.joint_ids] @@ -160,8 +191,16 @@ def rollout_ensemble(env, success_term, horizon, device): root_pose_w[:, 0:3], root_pose_w[:, 3:7], ee_pose_w[:, 0:3], ee_pose_w[:, 3:7] ) + desired_position = env.unwrapped.command_manager.get_command("object_pose") + + - # target EE pose in world -> convert to base/root frame + ## backup controller + #ee_recovery, gripper , state_guess= backup_controller.compute(ee_pose_w, object_pose_w, desired_position, desired_position, rest_pos, state_guess, 0) + ##ee_recovery, gripper , state_guess= backup_controller.compute(ee_pose=ee_pose_w, object_pose=object_pose_w) + print(f"state : {state_guess}") + #print(f"State machine returned :{ee_recovery}, {gripper}") + ee_recovery_pos, ee_recovery_rot = ee_recovery[:,0:3], ee_recovery[:,3:7] target_pos_w = ee_recovery_pos.to(device) target_quat_w = ee_recovery_rot.to(device) target_pos_b, target_quat_b = subtract_frame_transforms( @@ -172,10 +211,10 @@ def rollout_ensemble(env, success_term, horizon, device): # --- Translation delta (absolute -> small step) --- delta_pos_abs = target_pos_b - ee_pos_b # full error [N,3] # convert to small incremental command - pos_gain = 0.1 # tune: fraction of error to move per step + pos_gain = 0.2 # tune: fraction of error to move per step delta_pos = pos_gain * delta_pos_abs # clamp translation step (meters per step) - clamp = 0.01 + clamp = 0.02 delta_pos = torch.clamp(delta_pos, min=-clamp, max=clamp) # --- Rotation delta (correct quaternion math) --- @@ -195,48 +234,61 @@ def rollout_ensemble(env, success_term, horizon, device): # --- build controller command: [dx,dy,dz, droll,dpitch,dyaw] --- ee_goal = torch.cat([delta_pos, delta_rpy], dim=-1) # [N,6] - - # Debug prints (inspect magnitudes) - print(f"[DEBUG] ee_pos_b: {ee_pos_b}") - print(f"[DEBUG] target_pos_b: {target_pos_b}") - print(f"[DEBUG] delta_pos_abs norm: {torch.norm(delta_pos_abs).item():.4f}") - print(f"[DEBUG] delta_pos (sent): {delta_pos}") - print(f"[DEBUG] delta_rpy (sent, rad): {delta_rpy}") - print(f"[DEBUG] ee_goal (Δx,Δy,Δz,Δr,Δp,Δy): {ee_goal}") - - # --- set command on the existing controller (action cfg wraps controller) --- - # your diff_ik_action_cfg holds the controller at .controller diff_ctrl = diff_ik_controller.controller if hasattr(diff_ik_controller, "controller") else diff_ik_controller - + # ensure correct shape - assert ee_goal.dim() == 2 and ee_goal.shape[1] == diff_ctrl._command.shape[1], \ - f"Mismatch: ee_goal.shape={ee_goal.shape}, controller expects {diff_ctrl._command.shape}" - + #assert ee_goal.dim() == 2 and ee_goal.shape[1] == diff_ctrl._command.shape[1], \ + # f"Mismatch: ee_goal.shape={ee_goal.shape}, controller expects {diff_ctrl._command.shape}" + # print(f"\n[DEBUG] ee_pos_b: {ee_pos_b}") + # print(f"\n[DEBUG] target_pos_b: {target_pos_b}") + # print(f"\n[DEBUG] delta_pos_abs norm: {torch.norm(delta_pos_abs).item():.4f}") + # print(f"\n[DEBUG] delta_pos (sent): {delta_pos}") + # print(f"\n[DEBUG] delta_rpy (sent, rad): {delta_rpy}") + # print(f"\n[DEBUG] ee_goal (Δx,Δy,Δz,Δr,Δp,Δy): {ee_goal}") # set command and compute joint targets diff_ctrl.set_command(ee_goal, ee_pos_b, ee_quat_b) joint_pos_des = diff_ctrl.compute(ee_pos_b, ee_quat_b, jacobian, joint_pos) - + gripper_cmd = gripper.unsqueeze(0) + gripper_cmd = torch.cat([gripper_cmd, gripper_cmd], dim =-1) + #robot_action= torch.cat([joint_pos_des, gripper_cmd], dim=-1) + # print(f"\nSet this action for robot 0-8 {robot_action}") # --- apply joint targets via articulation API (do NOT call env.step(joint_pos_des)) --- - robot.set_joint_position_target(joint_pos_des, joint_ids=robot_entity_cfg.joint_ids) - + joint_ids=[0,1,2,3,4,5,6,7,8] + #robot.set_joint_position_target(robot_action, joint_ids=joint_ids) + + + # gripper + #robot.set_joint_position_target(gripper_cmd, joint_ids=gripper_entity_cfg.joint_ids) # advance the sim (two options - use direct sim step or call env.step with zero action) - env.scene.write_data_to_sim() - env.sim.step() + #env.scene.write_data_to_sim() + + # sim.read_data_from_sim() + #env.update_internal_state(env.dt) # update internal counters/variables + #obs_dict = env.get_observations() + # print(f"Obs {obs_dict}") + + # merges dict into one tensor - # If you need the gym env to also advance and return next obs, call env.step with a zero action: + print(f"ee_goal : {ee_goal}") + #print(f"joint_pos_des {joint_pos_des}") + + action=torch.cat([ee_goal, gripper.unsqueeze(0)], dim=-1) zero_action = torch.zeros(1, env.action_space.shape[1], device=device) - obs_dict, _, terminated, truncated, _ = env.step(zero_action) + #print(f"\npassed this action {action}") + obs_dict, _, terminated, truncated, _ = env.step(action) + #sim.step() + #obs_dict, _, terminated, truncated, _ = env.step(ee_goal) # check if we reached target (use absolute pos error) pos_err = torch.norm(target_pos_b - ee_pos_b) - print(f"[DEBUG] post-step pos_err (before update): {pos_err.item():.6f}") + # print(f"[DEBUG] post-step pos_err (before update): {pos_err.item():.6f}") if pos_err <= 1e-3: print("[Recovery] Target reached — ending recovery mode.") recovery_mode = False recovery_cooldown_active = True print("[Recovery] Cooldown Activated.") - + backup_controller.reset_idx() def main(): """Run a trained policy from robomimic with Isaac Lab environment.""" diff --git a/scripts/imitation_learning/robomimic/play_ensemble.py b/scripts/imitation_learning/robomimic/play_ensemble.py index 275f2199b98c..f9c8549b01de 100644 --- a/scripts/imitation_learning/robomimic/play_ensemble.py +++ b/scripts/imitation_learning/robomimic/play_ensemble.py @@ -94,10 +94,9 @@ from isaaclab.controllers import DifferentialIKController, DifferentialIKControllerCfg from isaaclab.utils.math import subtract_frame_transforms, euler_xyz_from_quat from isaaclab.managers import SceneEntityCfg -from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg - -#import chills.tasks -#from chills.tasks.mdp import upright_tilt_deg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg, BinaryJointPositionActionCfg +from isaaclab.envs.mdp.actions.binary_joint_actions import BinaryJointPositionAction +from backup_controller_handler import BackupController def rollout(policy, env, success_term, horizon, device): """Perform a single rollout of the policy in the environment. @@ -218,19 +217,7 @@ def has_fallen(asset, threshold: float = -0.7, debug: bool = True) -> bool: return uprightness > threshold -# this took me too long to work out i needed lol -def quaternion_to_euler(q_w, q_x, q_y, q_z): - # Roll (x-axis rotation) - roll = math.atan2(2 * (q_w * q_x + q_y * q_z), 1 - 2 * (q_x**2 + q_y**2)) - - # Pitch (y-axis rotation) - pitch = math.asin(2 * (q_w * q_y - q_z * q_x)) - - # Yaw (z-axis rotation) - yaw = math.atan2(2 * (q_w * q_z + q_x * q_y), 1 - 2 * (q_y**2 + q_z**2)) - - return roll, pitch, yaw def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, use_recovery=True, rollout_num=0): """Perform a single rollout of the policy in the environment. @@ -245,87 +232,55 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, terminated: Whether the rollout terminated. traj: The trajectory of the rollout. """ + ## do setup print("running rollout number : ", rollout_num) for policy in ensemble: policy.start_episode() obs_dict, _ = env.reset() - - #print(f"obs_dict type: {type(obs_dict)},\nobs_dict contents:\n {obs_dict}") - traj = dict(actions=[], obs=[], next_obs=[], sub_obs=[], uncertainties=[], min_actions=[], max_actions=[], time_taken=[], joint_pos=[], recovery=[], failure=[], grasp=[], appr=[]) ###### SETUP SWITCHING LOGIC #### switchingLogic = SwitchingLogic(parameters, horizon=horizon) - - sim = env.unwrapped.sim - num_envs = env.num_envs - - certain_joint_positions = [] - ### SET USE RECOVERY TO FALSE #### + ###### SET UP RECOVERY #### use_recovery=True recovery_activated_during_rollout = 0 - # Set up recovery controller - robot = env.unwrapped.scene["robot"] - print(f"Unwrapped sim : {sim}") - ee_recovery_pos = torch.tensor([[0.5206, 0.0096, 0.3751]], device=device) - - ee_recovery_rot = torch.tensor([[ 0.6664, 0.0360, 0.7414, -0.0705]], device=device) - # lets change this into the 6 element tensor they are expecting - roll,pitch,yaw = euler_xyz_from_quat(ee_recovery_rot) - ee_recovery_goal = torch.cat([ee_recovery_pos, ee_recovery_rot], dim =-1) - print(f"I got this roll {roll}, pitch {pitch}, yaw {yaw} and full tensor {torch.cat([roll, pitch, yaw], dim=-1)}\n i need to add with {ee_recovery_pos}") - ee_rpy = torch.cat([roll, pitch, yaw], dim=-1) - ee_rpy = ee_rpy.unsqueeze(0) - ee_goal = torch.cat([ee_recovery_pos, ee_rpy], dim=1) - # Create controller - diff_ik_cfg = DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls") - diff_ik_controller = DifferentialIKController(diff_ik_cfg, num_envs=env.unwrapped.scene.num_envs, device=sim.device) - - # diff_ik_action_controller = DifferentialInverseKinematicsActionCfg( - # asset_name="robot", - # joint_names=["panda_joint.*"], - # body_name="panda_hand", - # controller=diff_ik_controller, - # scale=0.5, - # body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), - # ) - robot_entity_cfg = SceneEntityCfg("robot", joint_names=["panda_joint.*"], body_names=["panda_hand"]) - robot_entity_cfg.resolve(env.unwrapped.scene) - if robot.is_fixed_base: - ee_jacobi_idx = robot_entity_cfg.body_ids[0] - 1 - else: - ee_jacobi_idx = robot_entity_cfg.body_ids[0] - -#########SET TO FALSE ####### - recovery_mode = True print("rollout recovery enabled ? : ", use_recovery) + ##### RECOVERY CONFIG #### recovery_cooldown_duration = 300 # timesteps recovery_cooldown_timer = recovery_cooldown_duration recovery_cooldown_active = False recovery = [] recovery_steps = 0 + + ##### CONFIG RECOVERY CONTROLLER #### + backup_controller = BackupController(env, device) + state_guess = 0 + last_state =0 + recovery_mode= False + state_guessed = False + recovery_triggered = False + object_knocked = False + print(f'Running for horizon {horizon}') for i in range(horizon): # Prepare observations obs = copy.deepcopy(obs_dict["policy"]) sub_obs = copy.deepcopy(obs_dict["subtask_terms"]) - object = env.unwrapped.scene["object"] - - # is_tipped = has_fallen(object, -0.95, True) + for ob in obs: obs[ob] = torch.squeeze(obs[ob]) - #print(f"found observation : {obs[ob]}") - + for subob in sub_obs: sub_obs[subob] = torch.squeeze(sub_obs[subob]) - if obs['object_knocked']: - print("object knocked over") + traj["obs"].append(obs) + traj["sub_obs"].append(sub_obs) + # Check if environment image observations if hasattr(env.cfg, "image_obs_list"): # Process image observations for robomimic inference @@ -338,156 +293,59 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, image = image.clip(0.0, 1.0) obs[image_name] = image - traj["obs"].append(obs) - traj["sub_obs"].append(sub_obs) - - object=env.unwrapped.scene['object'] - #tilt_deg = upright_tilt_deg(object.data.root_quat_w, object.data.default_root_state[:, 3:7]) - #print("Tilt deg : ", tilt_deg.item()) - if recovery_mode and not recovery_cooldown_active: - print(f"[Recovery] Mode Timestep {i}") - - jacobian = robot.root_physx_view.get_jacobians()[:, ee_jacobi_idx, :, robot_entity_cfg.joint_ids] - ee_pose_w = robot.data.body_pose_w[:, robot_entity_cfg.body_ids[0]] - root_pose_w = robot.data.root_pose_w - joint_pos = robot.data.joint_pos[:, robot_entity_cfg.joint_ids] - - # current EE pose in base frame - ee_pos_b, ee_quat_b = subtract_frame_transforms( - root_pose_w[:, 0:3], root_pose_w[:, 3:7], - ee_pose_w[:, 0:3], ee_pose_w[:, 3:7] - ) - print(f"ee_pos_b {ee_pos_b}, ee_quat_b : {ee_quat_b}") - - # target EE pose (absolute, world frame → base frame) - target_pos_w = ee_recovery_pos # [1,3] - target_quat_w = ee_recovery_rot # [1,4] - target_pos_b, target_quat_b = subtract_frame_transforms( - root_pose_w[:, 0:3], root_pose_w[:, 3:7], - target_pos_w, target_quat_w - ) - print(f"target_pos_b {target_pos_b}, target_quat_b {target_quat_b}") - - # --- compute relative pose command (6D) --- - # translation delta - delta_pos = target_pos_b - ee_pos_b # [1,3] - print(f"Delta pos {delta_pos}") - # rotation delta (ΔR = R_target * R_currentᵀ) - delta_rot = target_quat_b - ee_quat_b - print(f"Delat Rot {delta_rot}") - # convert ΔR to euler angles (XYZ convention) - r,p,y = euler_xyz_from_quat(delta_rot, wrap_to_2pi=False) - print(f"[DEBUG] Delat R {r}, P {p}, Y {y}") - delta_rpy = torch.cat([r,p,y], dim=-1) - delta_rpy = torch.unsqueeze(delta_rpy,0) - print(f"[DEBUG] RPY {delta_rpy}") - # --- build relative command --- - ee_goal = torch.cat([delta_pos, delta_rpy], dim=-1) # [1,6] - - - # debug info - print(f"[DEBUG] ee_goal (Δx,Δy,Δz,Δr,Δp,Δy): {ee_goal}") - print(f"[DEBUG] Current pos_b: {ee_pos_b}") - print(f"[DEBUG] Target pos_b: {target_pos_b}") - - # --- send command to controller --- - diff_ik_controller.set_command(ee_goal, ee_pos_b, ee_quat_b) - - # compute joint targets - joint_pos_des = diff_ik_controller.compute(ee_pos_b, ee_quat_b, jacobian, joint_pos) - - # (optional) denormalize actions if necessary - if args_cli.norm_factor_min is not None and args_cli.norm_factor_max is not None: - actions = ((actions + 1) * (args_cli.norm_factor_max - args_cli.norm_factor_min)) / 2 + args_cli.norm_factor_min - - # --- execute recovery movement --- - pos_err = torch.norm(target_pos_b - ee_pos_b) - actions=joint_pos_des - metrics = ensemble_uncertainty(ensemble, obs) - metrics['min']=actions - metrics['max']=actions - uncertainty = 0 - - if pos_err > 1e-3: - robot.set_joint_position_target(joint_pos_des, joint_ids=robot_entity_cfg.joint_ids) - env.scene.write_data_to_sim() - obs_dict, _, terminated, truncated, _ = env.step(joint_pos_des) - else: - print(f"[Recovery] Target reached — ending recovery mode.") - recovery_mode = False - recovery_cooldown_active = True - print("[Recovery] Cooldown Activated.") - - - else: - env.unwrapped.actions.arm_action = DifferentialInverseKinematicsActionCfg( - asset_name="robot", - joint_names=["panda_joint.*"], - body_name="panda_hand", - controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), - scale=0.5, - body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), - ) - recovery_steps=0 - # Calculate uncertainty using the ensemble - recovery.append(False) + #### get policy action #### + metrics = ensemble_uncertainty(ensemble, obs) + uncertainty = metrics['variance'] + policy_actions = metrics['mean'] + # fix state guess + if sub_obs['grasp']: + if state_guess <4 : + state_guess = 4 + + sm_actions , state_guess = backup_controller.get_controller_action(state_guess, 0) + if last_state != state_guess: + #print(f"State {state_guess.item()}") + last_state = state_guess - ## detect if we have tipped the object - - metrics = ensemble_uncertainty(ensemble, obs) - # Get the std and mean action - uncertainty = metrics['variance'] - actions = metrics['mean'] - # uncert went here - - - if switchingLogic.check(uncertainty, i) and not recovery_cooldown_active: - if use_recovery : - recovery_mode = True - recovery_activated_during_rollout+=1 - - #check halfway thorough, if we havent managed grasp, lets reset and try again - if switchingLogic.halfway_check(i,sub_obs['grasp'].item()): + # this is the switching logic + if switchingLogic.check(uncertainty, i): + # uncertainty triggered if use_recovery and not recovery_cooldown_active: + # if we have switched to recovery recovery_mode = True - - - if recovery_cooldown_active: - if object_grasped(env): - # normal recovery if already holding - recovery_cooldown_timer-=1 - else: - # shorten the cooldown for grasping stage - - recovery_cooldown_timer -= 1 + recovery_activated_during_rollout+=1 + if not recovery_triggered : + print(f"[RECOVERY] Step {i}") + recovery_triggered = True + if recovery_mode: + # if holding the object, start with lifting + actions = sm_actions + + else: + actions = policy_actions if recovery_cooldown_timer <= 0: recovery_cooldown_active = False recovery_cooldown_timer = recovery_cooldown_duration print("Recovery Cooldown Ended") - - if not recovery_mode: - current_absolute_joints = get_joint_pos(env) - certain_joint_positions.append(current_absolute_joints) - # Unnormalize actions if args_cli.norm_factor_min is not None and args_cli.norm_factor_max is not None: actions = ( (actions + 1) * (args_cli.norm_factor_max - args_cli.norm_factor_min) ) / 2 + args_cli.norm_factor_min - # print(f"before shaping action : {actions}") actions = actions.to(device=device).view(1, env.action_space.shape[1]) - # print(f"------------------------------") - # print(f"Policy action {actions.shape} : {actions}") - - # Apply actions - obs_dict, _, terminated, truncated, _ = env.step(actions) + + + # Apply actions + obs_dict, _, terminated, truncated, _ = env.step(actions) + recovery.append(recovery_mode) + # update obs from taking action obs = obs_dict["policy"] sub_obs = obs_dict["subtask_terms"] - # print(f"Obs change : {obs}") + # Record trajectory traj["actions"].append(actions.tolist()) traj["next_obs"].append(obs) @@ -497,6 +355,9 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, traj['time_taken'].append(metrics['time_taken']) traj['joint_pos'].append(obs['abs_joint_pos']) traj['recovery'].append(recovery) + if torch.any(obs['object_knocked']): + print("[MONITOR] object knocked over") + object_knocked = True # Check if rollout was successful if bool(success_term.func(env, **success_term.params)[0]): @@ -507,30 +368,30 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, traj['appr'].append(appr.item()) print(f"grasp : {traj['grasp']}, appr : {traj['appr']}") # print(f"grasp : {grasp.item()}, appr : {appr.item()}") - return True, traj, recovery_activated_during_rollout + return True, traj, recovery_activated_during_rollout, "" elif terminated or truncated: - grasp = torch.any(sub_obs['grasp']) traj['grasp'].append(grasp.item()) appr = torch.any(sub_obs['appr_goal']) traj['appr'].append(appr.item()) #print(f"grasp : {traj['grasp']}, appr : {traj['appr']}") - if obs['object_knocked']: - + if i==horizon: #if the failure was caused by object collision - traj['failure'].append("knocked") + traj['failure'].append("timeout") + failure_reason = "timeout" else: #if failure due to timeout, record timeout - traj['failure'].append("timeout") - return False, traj, recovery_activated_during_rollout + traj['failure'].append("knocked") + failure_reason = "object_knocked" + return False, traj, recovery_activated_during_rollout, failure_reason #print("REcovery : ", traj["recovery"]) traj['failure'].append("timeout") grasp = torch.any(sub_obs['grasp']) traj['grasp'].append(grasp.item()) appr = torch.any(sub_obs['appr_goal']) traj['appr'].append(appr.item()) - return False, traj, recovery_activated_during_rollout + return False, traj, recovery_activated_during_rollout, "" @@ -754,40 +615,41 @@ def main(): parameters = { 'beaker_lift' :{ 0 : { - "confidence_level": 0.017165569182596162, - "window_size": 5, - "max_peaks": 5 + "confidence_level": 0.010360572377319478, + "window_size": 9, + "max_peaks": 9 }, 1 : { - "confidence_level": 0.024839555704716736, - "window_size": 12, - "max_peaks": 4 + "confidence_level": 0.012117819935441015, + "window_size": 9, + "max_peaks": 9 }, 2 : { - "confidence_level": 0.022856649849482057, - "window_size": 12, - "max_peaks": 5 + "confidence_level": 0.0114854090040122, + "window_size": 9, + "max_peaks": 9 }, 3 : { - "confidence_level": 0.03564607457876469, + "confidence_level": 0.01721086513038985, "window_size": 9, - "max_peaks": 5 + "max_peaks": 9 }, 4 : { - "confidence_level": 0.03087440802734071, - "window_size": 10, - "max_peaks": 3 + "confidence_level": 0.01516669949421836, + "window_size": 9, + "max_peaks": 9 }, 5 : { - "confidence_level": 0.02323877457883784, - "window_size": 5, - "max_peaks": 4 + "confidence_level": 0.011445348395548671, + "window_size": 9, + "max_peaks": 9 }, 6 : { - "confidence_level": 0.8432899916370529, - "window_size": 13, - "max_peaks": 4 + "confidence_level": 0.0001330413469565007, + "window_size": 15, + "max_peaks": 9 }, + }, } @@ -831,7 +693,7 @@ def main(): for trial in range(args_cli.num_rollouts): print(f"[INFO] Starting trial {trial}") - terminated, traj, recovery_activated_during_rollout = rollout_ensemble(pick_place_ensemble[:args_cli.ensemble_size], env, success_term, args_cli.horizon, device, parameters['beaker_lift'], use_recovery=args_cli.use_recovery, rollout_num=trial) + terminated, traj, recovery_activated_during_rollout, failure = rollout_ensemble(pick_place_ensemble[:args_cli.ensemble_size], env, success_term, args_cli.horizon, device, parameters['beaker_lift'], use_recovery=args_cli.use_recovery, rollout_num=trial) # save the uncertainties print("Finished rollout, recovery needed : ", recovery_activated_during_rollout) #print("actions shape : ", traj['actions']) diff --git a/scripts/imitation_learning/robomimic/play_with_backup.py b/scripts/imitation_learning/robomimic/play_with_backup.py new file mode 100644 index 000000000000..2dda0f20fe31 --- /dev/null +++ b/scripts/imitation_learning/robomimic/play_with_backup.py @@ -0,0 +1,178 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# run this script: +# ./isaaclab.sh -p scripts/imitation_learning/robomimic/play_ensemble.py --device cuda --task Dev-IK-Rel-v0 --num_rollouts 100 +# stack cube task: ./isaaclab.sh -p scripts/imitation_learning/robomimic/play.py --device cuda --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --num_rollouts 50 +"""Script to play and evaluate a trained policy from robomimic. + +This script loads a robomimic policy and plays it in an Isaac Lab environment. + +Args: + task: Name of the environment. + checkpoint: Path to the robomimic policy checkpoint. + horizon: If provided, override the step horizon of each rollout. + num_rollouts: If provided, override the number of rollouts. + seed: If provided, overeride the default random seed. + norm_factor_min: If provided, minimum value of the action space normalization factor. + norm_factor_max: If provided, maximum value of the action space normalization factor. +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Evaluate robomimic policy for Isaac Lab environment.") +parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.") +parser.add_argument("--video_length", type=int, default=800, help="Length of the recorded video (in steps).") +parser.add_argument( + "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations." +) +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument("--checkpoint", type=str, default=None, help="Pytorch model checkpoint to load.") +parser.add_argument("--horizon", type=int, default=2000, help="Step horizon of each rollout.") +parser.add_argument("--num_rollouts", type=int, default=1, help="Number of rollouts.") +parser.add_argument("--seed", type=int, default=101, help="Random seed.") +parser.add_argument( + "--norm_factor_min", type=float, default=None, help="Optional: minimum value of the normalization factor." +) +parser.add_argument( + "--norm_factor_max", type=float, default=None, help="Optional: maximum value of the normalization factor." +) +parser.add_argument("--enable_pinocchio", default=False, action="store_true", help="Enable Pinocchio.") + +parser.add_argument("--model_name", type=str, default=None) + +group = parser.add_mutually_exclusive_group() +group.add_argument("--use_recovery", type=bool ,default= False, help="Enable recovery mechanism. By default recovery is enabled.") + +parser.add_argument("--ensemble_size", type=int, default=10) + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +if args_cli.enable_pinocchio: + # Import pinocchio before AppLauncher to force the use of the version installed by IsaacLab and not the one installed by Isaac Sim + # pinocchio is required by the Pink IK controllers and the GR1T2 retargeter + import pinocchio # noqa: F401 +import numpy as np +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import time +import copy +import random +import os +import gymnasium as gym +import torch +import math +from collections import deque + +import robomimic.utils.file_utils as FileUtils +import robomimic.utils.torch_utils as TorchUtils +from isaaclab_tasks.manager_based.manipulation.cube_lift.mdp.observations import get_joint_pos, object_grasped +#from chills.tasks.mdp.observations import get_joint_pos, object_grasped +if args_cli.enable_pinocchio: + import isaaclab_tasks.manager_based.manipulation.pick_place # noqa: F401 + +from isaaclab_tasks.utils import parse_env_cfg +#from isaaclab.utils.logging_helper import LoggingHelper, ErrorType, LogType +from isaaclab.utils.math import matrix_from_quat, convert_quat, quat_mul, euler_xyz_from_quat +from evaluation import ensemble_uncertainty +from isaaclab.safety.switchingLogic import SwitchingLogic +from isaaclab.controllers import DifferentialIKController, DifferentialIKControllerCfg +from isaaclab.utils.math import subtract_frame_transforms, euler_xyz_from_quat +from isaaclab.managers import SceneEntityCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg, BinaryJointPositionActionCfg +from isaaclab.envs.mdp.actions.binary_joint_actions import BinaryJointPositionAction +from backup_controller_handler import BackupController + + +def rollout_ensemble(env, success_term, horizon, device): + """Perform a single rollout of the policy in the environment. + Args: + policy: The robomimicpolicy to play. + env: The environment to play in. + horizon: The step horizon of each rollout. + device: The device to run the policy on. + Returns: + terminated: Whether the rollout terminated. + traj: The trajectory of the rollout. + """ + env.reset() + ###### SETUP SWITCHING LOGIC #### + + + ### SET USE RECOVERY TO FALSE #### + # Set up recovery controller + + + backup_controller = BackupController(env, device) + state_guess = 0 + for i in range(horizon): + + + action, state_guess = backup_controller.get_controller_action(state_guess, 0) + print(f"state guess : {state_guess}") + + zero_action = torch.zeros(1, env.action_space.shape[1], device=device) + #print(f"\npassed this action {action}") + obs_dict, _, terminated, truncated, _ = env.step(action) + #sim.step() + #obs_dict, _, terminated, truncated, _ = env.step(ee_goal) + + # check if we reached target (use absolute pos error) + + # print(f"[DEBUG] post-step pos_err (before update): {pos_err.item():.6f}") + + + backup_controller.reset() + +def main(): + """Run a trained policy from robomimic with Isaac Lab environment.""" + # parse configuration + env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=1, use_fabric=not args_cli.disable_fabric) + + # Set observations to dictionary mode for Robomimic + env_cfg.observations.policy.concatenate_terms = False + #create a log handler + # loghelper = LoggingHelper() + # Disable recorder + env_cfg.recorders = None + # Extract success checking function + success_term = env_cfg.terminations.success + env_cfg.terminations.success = None + arm_action = env_cfg.actions.arm_action + # Get timeout signal + timeout = env_cfg.terminations.time_out + #env_cfg.terminations.time_out = None + # Create environment + env = gym.make(args_cli.task, cfg=env_cfg).unwrapped + # Set seed9 + torch.manual_seed(args_cli.seed) + env.seed(args_cli.seed) + # Acquire device + device = TorchUtils.get_torch_device(try_to_use_cuda=True) + for trial in range(args_cli.num_rollouts): + print(f"[INFO] Starting trial {trial}") + + rollout_ensemble( env, success_term, args_cli.horizon, device) + # save the uncertainties + env.close() + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() \ No newline at end of file diff --git a/source/isaaclab/isaaclab/controllers/differential_ik.py b/source/isaaclab/isaaclab/controllers/differential_ik.py index e10ff6d9cbde..e6e623a5cb37 100644 --- a/source/isaaclab/isaaclab/controllers/differential_ik.py +++ b/source/isaaclab/isaaclab/controllers/differential_ik.py @@ -117,7 +117,7 @@ def set_command( ValueError: If the command type is ``pose_rel`` and either :attr:`ee_pos` or :attr:`ee_quat` is None. """ # store command - #print(f"[DEBUG] command : {command}") + # print(f"\n[DEBUG IK] command : {command}") self._command[:] = command # compute the desired end-effector pose diff --git a/source/isaaclab/isaaclab/envs/mdp/actions/binary_joint_actions.py b/source/isaaclab/isaaclab/envs/mdp/actions/binary_joint_actions.py index 501c221dec6a..5d76ecc85a9b 100644 --- a/source/isaaclab/isaaclab/envs/mdp/actions/binary_joint_actions.py +++ b/source/isaaclab/isaaclab/envs/mdp/actions/binary_joint_actions.py @@ -126,6 +126,7 @@ def IO_descriptor(self) -> GenericActionIODescriptor: """ def process_actions(self, actions: torch.Tensor): + #print(f"recieved gripper actions { actions}") # store the raw actions self._raw_actions[:] = actions # compute the binary mask diff --git a/source/isaaclab/isaaclab/safety/switchingLogic.py b/source/isaaclab/isaaclab/safety/switchingLogic.py index f2aa069102c1..86dcc7e5030a 100644 --- a/source/isaaclab/isaaclab/safety/switchingLogic.py +++ b/source/isaaclab/isaaclab/safety/switchingLogic.py @@ -13,6 +13,7 @@ def __init__(self, parameters, joints_to_check:list[int] =[0, 1, 2, 3, 4, 5, 6] self.unsafe_windows = {joint_num : 0 for joint_num in self.joints} self.peak_limit = peak_limit self.window_confidence_level = 0.669 + self.triggered = False def _calc_windows(self)-> list[deque] : @@ -22,7 +23,9 @@ def _calc_windows(self)-> list[deque] : def halfway_check(self, step:int=0, grasp_state:bool=True): #check that the subtask has been comepleted if (step==(round(self.horizon/2))) and not grasp_state: - print(f'I got halfway and hadnt done subtask, lets try again') + if not self.triggered: + print(f'I got halfway and hadnt done subtask, lets try again') + self.triggered =True #then we are halfway through and lost, try again return True return False @@ -40,7 +43,9 @@ def check(self, uncertainties, step) -> bool: # print(f'joint {joint_num}, window: {test_window}') # print(f"manually found peaks : {peaks}") if peaks>=self.params[joint_num]['max_peaks']: - print(f"Uncertaity in joint {joint_num} triggered") + if not self.triggered: + print(f"Uncertaity in joint {joint_num} triggered") + self.triggered =True return True diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py index e17a95fa38d0..236c61480980 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py @@ -22,6 +22,7 @@ from isaaclab.utils import configclass from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR import warp as wp +import math from . import mdp #from isaaclab.utils.logging_helper import LoggingHelper, ErrorType, LogType @@ -95,7 +96,7 @@ class CommandsCfg: debug_vis=True, ranges=mdp.UniformPoseCommandCfg.Ranges( # pos_x=(0.3,0.3), pos_y=(-0.28, -0.28), pos_z=(0.1, 0.1), roll=(0.0, 0.0), pitch=(0.0, 0.0), yaw=(0.0, 0.0) - pos_x=(0.4,0.4), pos_y=(-0.3, -0.3), pos_z=(0.08, 0.08), roll=(0.0, 0.0), pitch=(0.0, 0.0), yaw=(0.0, 0.0) + pos_x=(0.4,0.4), pos_y=(-0.3, -0.3), pos_z=(0.08, 0.08), roll=(math.pi/2, math.pi/2), pitch=(math.pi/2,math.pi/2), yaw=(0.0, 0.0) ), ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py index 7dce6fe950a8..6e12df6cf39c 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py @@ -304,6 +304,6 @@ def object_knocked(env: ManagerBasedRLEnv, upper_object_cfg: SceneEntityCfg = Sc object: RigidObject = env.scene[upper_object_cfg.name] tilt_deg = upright_tilt_deg(object.data.root_quat_w, object.data.default_root_state[:, 3:7]) if tilt_deg.cpu().detach().numpy()[0] > max_tilt: - print("object knocked") + # print("object knocked") return torch.tensor([True], device='cuda:0') else :return torch.tensor([False], device='cuda:0') \ No newline at end of file From 4051543dffbd905dbc4b64d1b68dba92ba57ee97 Mon Sep 17 00:00:00 2001 From: LJones-alt Date: Mon, 24 Nov 2025 11:59:38 +0000 Subject: [PATCH 07/15] backup controller update --- .../robomimic/backup_controller_handler.py | 3 +++ .../imitation_learning/robomimic/play_ensemble.py | 2 +- .../cube_lift/config/franka/__init__.py | 14 ++++++++++++++ .../config/franka/dev_ik_rel_env_place.py | 3 ++- 4 files changed, 20 insertions(+), 2 deletions(-) diff --git a/scripts/imitation_learning/robomimic/backup_controller_handler.py b/scripts/imitation_learning/robomimic/backup_controller_handler.py index 261fc906396a..e3b336e1bfd3 100644 --- a/scripts/imitation_learning/robomimic/backup_controller_handler.py +++ b/scripts/imitation_learning/robomimic/backup_controller_handler.py @@ -40,6 +40,9 @@ def _get_rest_pos(self): # lets change this into the 6 element tensor they are expecting roll,pitch,yaw = euler_xyz_from_quat(ee_recovery_rot) rest_pos = torch.cat([rest_pos, ee_recovery_rot], dim =-1) + print(f"Rest pos {rest_pos}") + print(f"rpy : {roll}, {pitch}, {yaw}") + #print(f"rpy version :{torch.cat([rest_pos,roll.unsqueeze(0), pitch.unsqueeze(0), yaw.unsqueeze(0)], dim =-1)}") return rest_pos def _setup_robot(self): diff --git a/scripts/imitation_learning/robomimic/play_ensemble.py b/scripts/imitation_learning/robomimic/play_ensemble.py index f9c8549b01de..fea3fdc51d5c 100644 --- a/scripts/imitation_learning/robomimic/play_ensemble.py +++ b/scripts/imitation_learning/robomimic/play_ensemble.py @@ -597,7 +597,7 @@ def main(): # Create environment env = gym.make(args_cli.task, cfg=env_cfg).unwrapped - # Set seed9 + # Set seed torch.manual_seed(args_cli.seed) env.seed(args_cli.seed) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py index 4059bfcf8045..16d4e657c655 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py @@ -140,3 +140,17 @@ disable_env_checker=True, ) +gym.register( + id="Dev-IK-Rel-Place-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.dev_ik_rel_env_place:FrankaDevEnvCfg", + + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), + "robomimic_bc_trans_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_trans.json"), + "robomimic_hbc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/hbc.json"), + "robomimic_bcq_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bcq.json"), + + }, + disable_env_checker=True, +) \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py index f63cbb50b0bd..2555e6d49938 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py @@ -28,7 +28,8 @@ def __post_init__(self): # put the beaker on the stir plate glassware = ChemistryGlassware() self.scene.stirplate = glassware.stirplate(pos=[0.5, 0.0, 0.01]) - + self.scene.scale = glassware.scale(pos=[0.3, -0.3, 0.01]) + self.events.reset_object_position = EventTerm( func=mdp.reset_multiple_root_state_uniform, mode="reset", From 24a4ed22fd3e841dc19358d7e15ff333161d2801 Mon Sep 17 00:00:00 2001 From: LJones-alt Date: Thu, 4 Dec 2025 08:43:39 +0000 Subject: [PATCH 08/15] addec copilot instructions --- .github/copilot-instructions.md | 205 ++++++++++++++++++++++++++++++++ 1 file changed, 205 insertions(+) create mode 100644 .github/copilot-instructions.md diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md new file mode 100644 index 000000000000..16a5d0b2481c --- /dev/null +++ b/.github/copilot-instructions.md @@ -0,0 +1,205 @@ +# AI Coding Agent Instructions for IsaacLab-Laura + +## Project Overview + +**IsaacLab-Laura** is a robotics manipulation research framework built on [IsaacLab](https://github.com/isaac-sim/IsaacLab), focused on **Chemistry Inspired Manipulation Learning Lab Skills (ChILLS)** — a sim-to-real imitation learning benchmark for lab object manipulation (lift, place, pour tasks). + +- **Base:** NVIDIA Isaac Sim + IsaacLab 2.0 (GPU-accelerated physics simulation) +- **Frontend:** Python 3.11, manager-based environments, HDF5 datasets, RoboMimic training +- **Entry point:** `isaaclab.sh` launcher script; ChILLS in `source/chills/` +- **Current branch:** `skillgen_demo` + +--- + +## Critical Architecture Patterns + +### 1. **Configuration-Driven Design via `@configclass`** +- All tasks, environments, and agents use `isaaclab.utils.configclass` decorator +- No `__init__` in config classes; prefer dataclass field defaults +- Example: `FrankaIkEnvCfg` extends `ManagerBasedRLEnvCfg`, sets `sim.dt=0.01`, `decimation=2` +- **Location:** `source/chills/chills/envs/franka_env.py`, `source/isaaclab_tasks/` +- **Key convention:** Use `__post_init__()` to override parent configs cleanly + +### 2. **Manager-Based Environment Workflow** (not DirectRL) +- ChILLS uses **manager-based** environments with modular MDP components: + - **Observations:** `observations_cfg()` → stacked obs terms (e.g., ee_pos, object_pos) + - **Actions:** `FrankaActionsCfg` → maps deltas to joint commands + - **Rewards:** `rewards_cfg()` → scalar reward per step + - **Terminations:** `terminations_cfg()` → episode end conditions (success, failure, time) +- Each task inherits from `ManagerBasedRLEnvCfg` and composes scene + MDP configs +- **Location:** `source/chills/chills/envs/`, `source/chills/chills/tasks/` + +### 3. **Data Pipeline: 1_recorded → 2_annotated → 3_collected → Gen** +- **1_recorded:** Raw human demos (HDF5) from teleop (`scr/1_record.py`) +- **2_annotated:** Trimmed, labeled with key events (`scr/2_annotate.py`) +- **3_collected:** Consolidated canonical dataset (`scr/3_consolidate.py`) +- **Generated:** Mimic-augmented demos (×10–100×) in `datasets/chills_lfs/` (`scr/4_generate.py`) +- **Training:** Uses any collected/generated HDF5; outputs to `models/runs////` +- **File naming convention:** `_dataset24x.hdf5`, `_collected.hdf5`, `0tokGen.hdf5` + +### 4. **Mimic Augmentation (Data Generation)** +- Uses `isaaclab_mimic` extension (separate from core ChILLS tasks) +- Converts **BC (behavioral cloning) dataset** → **large synthetic episodes** via in-sim randomization +- Special env variants: `LiftLabwareMimic-v0`, `PlaceLabwareMimic-v0` (mimic-only) +- Key config: `env_cfg.datagen_config` with `generation_num_trials`, `generation_keep_failed`, `generation_relative` +- **Location:** `source/isaaclab_mimic/isaaclab_mimic/datagen/`, `source/chills/chills/envs/` + +### 5. **Quaternion Convention: wxyz (Always)** +- Isaac Lab uses **wxyz** (w=scalar, xyz=vector) +- Never mix with xyzw or Euler angles without explicit conversion +- Use `isaaclab.utils.math` for transforms; do not roll custom conversions +- Example: `rot=[0.707, 0, 0, 0.707]` is 90° around z-axis + +--- + +## Build & Execution Workflows + +### Environment Setup +```bash +# Create conda/uv environment (Isaac Sim 4.5 or 5.0) +./isaaclab.sh -c env_isaaclab # conda (auto-selects Python 3.10 for sim 4.5) +# OR +./isaaclab.sh -u env_isaaclab # uv + +# Activate, then install ChILLS +conda activate env_isaaclab +cd source/chills +pip install -e . +``` + +### Core Scripts (Numbered Pipeline) +All scripts in `source/chills/chills/scr/` follow a consistent CLI pattern (via `mods/app_parser.py`): + +| Script | Purpose | Example | +|--------|---------|---------| +| `0_teleop.py` | Device setup & sanity checks | `python scr/0_teleop.py --task LiftLabware-v0 --teleop_device gamepad` | +| `1_record.py` | Record human demos | `python scr/1_record.py --task LiftLabware-v0 --dataset_file datasets/LiftLabware/1_recorded/10x.hdf5 --num_demos 10` | +| `2_annotate.py` | Label key events, filter | `python scr/2_annotate.py --input_file ... --output_file ... --auto --num_success_steps 30` | +| `3_consolidate.py` | Merge multiple HDF5s | `python scr/3_consolidate.py --inputs "datasets/*/2_annotated/*.hdf5" --output_file ... ` | +| `4_generate.py` | Mimic augmentation | `python scr/4_generate.py --task LiftLabwareMimic-v0 --num_envs 128 --generation_num_trials 4000 --input_file ... --output_file ...` | +| `5_replay.py` | Visualize dataset episodes | `python scr/5_replay.py --task LiftLabware-v0 --input_file ... --sample 5` | +| `6_add_key_obs.py` | Inject observation keys | `python scr/6_add_key_obs.py --input_file ... --output_file ... --keys ...` | +| `7_train.py` | Train RoboMimic models | `python scr/7_train.py --task PlaceLabware-v0 --dataset ... --algo bc_rnn --seed 0 --device cuda` | +| `8_play.py` | Run trained checkpoint | `python scr/8_play.py --task PlaceLabware-v0 --checkpoint models/runs/.../best_val.pth --episodes 10` | +| `9_gather_best_val.py` | Harvest best checkpoints | `python scr/9_gather_best_val.py --runs_root runs/ --dest_root best_runs/` | + +### High-Level Orchestrators +- **`run.py`** (authoritative): `python run.py pipeline -E PlaceLabware -A bc_rnn bc_trans --runner auto` +- **`go.sh`** (bash): `ENV_NAME=PlaceLabware ./go.sh 4 6` (steps 4=generate, 6=train) +- **`auto.ps1`** (PowerShell): `./auto.ps1 -EnvName PlaceLabware -Algos bc_rnn -Script 4,6` + +**Always use `isaaclab.sh -p` (via `--runner auto`) to execute Python scripts** so Isaac Sim environment is properly loaded. + +--- + +## Project-Specific Conventions + +### 1. **Naming & File Organization** +- **Tasks:** `Labware`, e.g., `LiftLabware-v0`, `PlaceLabware-v0` (gym registry) +- **Task classes:** `source/chills/chills/tasks/lift_labware.py` +- **Task configs:** `source/chills/chills/tasks/_env_cfg.py` +- **Datasets root:** `datasets//[1_recorded|2_annotated|3_collected]/` +- **Generated datasets:** `datasets/chills_lfs//toGen.hdf5` +- **Model checkpoints:** `models/runs/////best_val_model.pth` + +### 2. **Asset & Labware Factory** +- USD labware stored in `source/chills/chills/assets/` (e.g., `beaker/beaker.usd`) +- Factory functions: `source/chills/chills/envs/labware_cfg.py` → `beaker()`, `conical_flask()`, `hotplate()`, `scale()` +- All assets use semantic tags: `[("class", "beaker")]` +- Frame transforms: Use `FrameTransformerCfg` with `OffsetCfg` for EE-relative observations + +### 3. **Environment Control: Absolute Targets ↔ Delta Actions** +- **ChillsMimicEnv** wraps base env to convert: + - Absolute EE target pose → delta joint commands (IK-resolved) + - Subtask logic: phases, success checks, object reference tracking +- EE frame always in **world coordinates**; observations translated to EE-relative +- **Location:** `source/isaaclab_mimic/isaaclab_mimic/envs/ChillsMimicEnv.py` + +### 4. **RoboMimic Training Algorithms** +Supported configs in `source/chills/chills/tasks/agents/robomimic/`: +- **BC-RNN:** `bc_rnn.json` (recurrent policy, good for long horizons) +- **BC-Transformer:** `bc_trans.json` (transformer-based, preferred for longer sequences) +- **HBC:** `hbc.json` (hierarchical behavior cloning) +- Upcoming: Diffusion models (RoboMimic ≥0.5.0) + +### 5. **Logging & Reproducibility** +- TensorBoard logs: `datasets//logs/` or `models/runs////` +- Script execution logs: `logs/script_logger.md` (managed by `run.py`) +- **Always fix seed** for reproducible runs: `--seed ` +- Build version tracking: `isaaclab.sh` detects Isaac Sim 4.5 vs 5.0; logs printed at startup + +### 6. **Headless vs Viewer Rendering** +- `--headless` for large-scale data generation (faster, no GPU overhead for display) +- Viewer mode for debugging / sanity checks; default eye: `(1.0, 1.0, 1.0)`, lookat: `(0.0, 0.0, -0.1)` +- Translucent rendering enabled by default in base Franka config for glass/fluid assets + +--- + +## Key Files & Where to Look + +| Purpose | Location | +|---------|----------| +| Base Franka scene & env config | `source/chills/chills/envs/franka_env.py` | +| Task definitions (lift, place, pour) | `source/chills/chills/tasks/lift_labware.py`, `place_labware.py` | +| MDP module examples | `source/chills/chills/envs/mdp/` (observations, rewards, terminations) | +| Labware factory & presets | `source/chills/chills/envs/labware_cfg.py` | +| Common CLI argument parser | `source/chills/chills/scr/mods/app_parser.py` | +| Mimic env wrapper | `source/isaaclab_mimic/isaaclab_mimic/envs/ChillsMimicEnv.py` | +| Data generation orchestrator | `source/isaaclab_mimic/isaaclab_mimic/datagen/generation.py` | +| Task registration & gym entry points | `source/isaaclab_tasks/isaaclab_tasks/` | +| Isaac Lab core docs & reference arch | `docs/source/refs/reference_architecture/index.rst` | + +--- + +## Common Debugging Patterns + +1. **"No success termination term found"** → Ensure task config has `terminations_cfg()` with `success` term +2. **Quaternion rotation looks wrong** → Verify wxyz ordering; check frame transform direction +3. **Viewer hangs or TLAS warnings** → Use `--headless` or reduce scene complexity; verify GPU driver +4. **USD asset missing** → Confirm file exists at `source/chills/chills/assets//.usd` +5. **Dataset file mismatch** → Filename must match pipeline expectation (`_recorded`, `_annotated`, `_collected`, `Gen`) +6. **Training converges poorly** → Check observation scaling, action ranges, reward shaping; verify dataset quality with `5_replay.py` + +--- + +## Testing & Validation + +- **Unit tests:** `source/chills/chills/test.py` or pytest in `tools/` +- **Data sanity checks:** Always replay a random sample before training: `python scr/5_replay.py --sample 5` +- **Pre-commit hooks:** `./isaaclab.sh -f` runs formatters/linters (isort, black, flake8) +- **Pytest:** `./isaaclab.sh -t` runs all tests with timeout guards + +--- + +## External Dependencies & Integration Points + +- **IsaacLab core:** `isaaclab.*` (assets, envs, sensors, sim utils) +- **Isaac Sim binary:** Loaded via `_isaac_sim/` symlink or pip (`isaacsim-rl`) +- **RoboMimic:** Dataset loading, policy training, inference in `isaaclab_mimic` +- **HDF5 I/O:** `h5py` for demo storage & augmentation +- **Hydra:** Config management in IsaacLab tasks (not used in ChILLS scripts directly) +- **Gym registry:** Standard `gymnasium` for environment registration + +--- + +## Writing New Code + +When adding features (e.g., new task, MDP module): + +1. **Define config class** with `@configclass` decorator (no `__init__`) +2. **Inherit from appropriate base** (`ManagerBasedRLEnvCfg`, `SubTaskConfig`, etc.) +3. **Use official Isaac Lab utilities** (`isaaclab.utils.math`, `isaaclab.sim.*`) — don't roll custom transforms +4. **Maintain naming conventions** (`Labware-v0`, `_env_cfg.py`, `_collected.hdf5`) +5. **Add docstrings** to modules & classes (author, purpose, high-level design) +6. **Log execution** via existing patterns (`scr/mods/logger.py` or native Python logging) +7. **Test locally** with `--headless` before submitting; commit with passing unit tests + +--- + +## References + +- **ChILLS README:** `source/chills/README.md` (quick-start, pipeline overview) +- **ChILLS scripts README:** `source/chills/chills/scr/README.md` (detailed per-script guide) +- **IsaacLab docs:** `docs/source/refs/reference_architecture/index.rst` (architecture walkthrough) +- **PyProject config:** `pyproject.toml` (import sections, tool configs, version constraints) +- **Launcher script:** `isaaclab.sh` (env detection, Isaac Sim path resolution, Python exe extraction) From e85da645b0be2fd77a9d126cf587ce7a2ab521af Mon Sep 17 00:00:00 2001 From: LJones-alt Date: Fri, 5 Dec 2025 17:01:10 +0000 Subject: [PATCH 09/15] idk how i messed that up, teleop fixed now --- docker/docker-compose.yaml | 8 +- .../teleoperation/teleop_isaaclab_env.py | 263 ++++++++++++++++++ .../teleoperation/teleop_se3_agent.py | 2 +- .../robomimic/play_ensemble.py | 2 +- .../config/franka/dev_ik_rel_env_place.py | 31 ++- .../manipulation/cube_lift/lift_env_cfg.py | 2 + .../cube_lift/mdp/observations.py | 4 +- 7 files changed, 290 insertions(+), 22 deletions(-) create mode 100644 scripts/environments/teleoperation/teleop_isaaclab_env.py diff --git a/docker/docker-compose.yaml b/docker/docker-compose.yaml index 1f14102eced6..f1cf86c2daa9 100644 --- a/docker/docker-compose.yaml +++ b/docker/docker-compose.yaml @@ -112,8 +112,8 @@ services: tty: true devices: - /dev/hidraw0:/dev/hidraw0 - # - /dev/hidraw1:/dev/hidraw1 - # - /dev/hidraw2:/dev/hidraw2 + - /dev/hidraw1:/dev/hidraw1 + - /dev/hidraw2:/dev/hidraw2 # This service adds a ROS2 Humble # installation on top of the base image @@ -145,8 +145,8 @@ services: tty: true devices: - /dev/hidraw0:/dev/hidraw0 - # - /dev/hidraw1:/dev/hidraw1 - # - /dev/hidraw2:/dev/hidraw2 + - /dev/hidraw1:/dev/hidraw1 + - /dev/hidraw2:/dev/hidraw2 volumes: # isaac-sim diff --git a/scripts/environments/teleoperation/teleop_isaaclab_env.py b/scripts/environments/teleoperation/teleop_isaaclab_env.py new file mode 100644 index 000000000000..021ee5ff80ff --- /dev/null +++ b/scripts/environments/teleoperation/teleop_isaaclab_env.py @@ -0,0 +1,263 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to run a keyboard teleoperation with Isaac Lab manipulation environments.""" + +"""Launch Isaac Sim Simulator first.""" + +import argparse +from collections.abc import Callable + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Keyboard teleoperation for Isaac Lab environments.") +parser.add_argument("--num_envs", type=int, default=1, help="Number of environments to simulate.") +parser.add_argument( + "--teleop_device", + type=str, + default="keyboard", + help="Device for interacting with environment. Examples: keyboard, spacemouse, gamepad, handtracking, manusvive", +) +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument("--sensitivity", type=float, default=1.0, help="Sensitivity factor.") +parser.add_argument( + "--enable_pinocchio", + action="store_true", + default=False, + help="Enable Pinocchio.", +) +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +app_launcher_args = vars(args_cli) + +if args_cli.enable_pinocchio: + # Import pinocchio before AppLauncher to force the use of the version installed by IsaacLab and + # not the one installed by Isaac Sim pinocchio is required by the Pink IK controllers and the + # GR1T2 retargeter + import pinocchio # noqa: F401 +if "handtracking" in args_cli.teleop_device.lower(): + app_launcher_args["xr"] = True + +# launch omniverse app +app_launcher = AppLauncher(app_launcher_args) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + + +import gymnasium as gym +import torch + +import omni.log + +from isaaclab.devices import Se3Gamepad, Se3GamepadCfg, Se3Keyboard, Se3KeyboardCfg, Se3SpaceMouse, Se3SpaceMouseCfg +from isaaclab.devices.openxr import remove_camera_configs +from isaaclab.devices.teleop_device_factory import create_teleop_device +from isaaclab.managers import TerminationTermCfg as DoneTerm + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.manager_based.manipulation.lift import mdp +from isaaclab_tasks.utils import parse_env_cfg + +if args_cli.enable_pinocchio: + import isaaclab_tasks.manager_based.manipulation.pick_place # noqa: F401 + + +def main() -> None: + """ + Run keyboard teleoperation with Isaac Lab manipulation environment. + + Creates the environment, sets up teleoperation interfaces and callbacks, + and runs the main simulation loop until the application is closed. + + Returns: + None + """ + # parse configuration + env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=args_cli.num_envs) + env_cfg.env_name = args_cli.task + # modify configuration + env_cfg.terminations.time_out = None + if "Lift" in args_cli.task: + # set the resampling time range to large number to avoid resampling + env_cfg.commands.object_pose.resampling_time_range = (1.0e9, 1.0e9) + # add termination condition for reaching the goal otherwise the environment won't reset + env_cfg.terminations.object_reached_goal = DoneTerm(func=mdp.object_reached_goal) + + if args_cli.xr: + # External cameras are not supported with XR teleop + # Check for any camera configs and disable them + env_cfg = remove_camera_configs(env_cfg) + env_cfg.sim.render.antialiasing_mode = "DLSS" + + try: + # create environment + env = gym.make(args_cli.task, cfg=env_cfg).unwrapped + # check environment name (for reach , we don't allow the gripper) + if "Reach" in args_cli.task: + omni.log.warn( + f"The environment '{args_cli.task}' does not support gripper control. The device command will be" + " ignored." + ) + except Exception as e: + omni.log.error(f"Failed to create environment: {e}") + simulation_app.close() + return + + # Flags for controlling teleoperation flow + should_reset_recording_instance = False + teleoperation_active = True + + # Callback handlers + def reset_recording_instance() -> None: + """ + Reset the environment to its initial state. + + Sets a flag to reset the environment on the next simulation step. + + Returns: + None + """ + nonlocal should_reset_recording_instance + should_reset_recording_instance = True + print("Reset triggered - Environment will reset on next step") + + def start_teleoperation() -> None: + """ + Activate teleoperation control of the robot. + + Enables the application of teleoperation commands to the environment. + + Returns: + None + """ + nonlocal teleoperation_active + teleoperation_active = True + print("Teleoperation activated") + + def stop_teleoperation() -> None: + """ + Deactivate teleoperation control of the robot. + + Disables the application of teleoperation commands to the environment. + + Returns: + None + """ + nonlocal teleoperation_active + teleoperation_active = False + print("Teleoperation deactivated") + + # Create device config if not already in env_cfg + teleoperation_callbacks: dict[str, Callable[[], None]] = { + "R": reset_recording_instance, + "START": start_teleoperation, + "STOP": stop_teleoperation, + "RESET": reset_recording_instance, + } + + # For hand tracking devices, add additional callbacks + if args_cli.xr: + # Default to inactive for hand tracking + teleoperation_active = False + else: + # Always active for other devices + teleoperation_active = True + + # Create teleop device from config if present, otherwise create manually + teleop_interface = None + try: + if hasattr(env_cfg, "teleop_devices") and args_cli.teleop_device in env_cfg.teleop_devices.devices: + teleop_interface = create_teleop_device( + args_cli.teleop_device, env_cfg.teleop_devices.devices, teleoperation_callbacks + ) + else: + omni.log.warn(f"No teleop device '{args_cli.teleop_device}' found in environment config. Creating default.") + # Create fallback teleop device + sensitivity = args_cli.sensitivity + if args_cli.teleop_device.lower() == "keyboard": + teleop_interface = Se3Keyboard( + Se3KeyboardCfg(pos_sensitivity=0.05 * sensitivity, rot_sensitivity=0.05 * sensitivity) + ) + elif args_cli.teleop_device.lower() == "spacemouse": + teleop_interface = Se3SpaceMouse( + Se3SpaceMouseCfg(pos_sensitivity=0.05 * sensitivity, rot_sensitivity=0.05 * sensitivity) + ) + elif args_cli.teleop_device.lower() == "gamepad": + teleop_interface = Se3Gamepad( + Se3GamepadCfg(pos_sensitivity=0.1 * sensitivity, rot_sensitivity=0.1 * sensitivity) + ) + else: + omni.log.error(f"Unsupported teleop device: {args_cli.teleop_device}") + omni.log.error("Supported devices: keyboard, spacemouse, gamepad, handtracking") + env.close() + simulation_app.close() + return + + # Add callbacks to fallback device + for key, callback in teleoperation_callbacks.items(): + try: + teleop_interface.add_callback(key, callback) + except (ValueError, TypeError) as e: + omni.log.warn(f"Failed to add callback for key {key}: {e}") + except Exception as e: + omni.log.error(f"Failed to create teleop device: {e}") + env.close() + simulation_app.close() + return + + if teleop_interface is None: + omni.log.error("Failed to create teleop interface") + env.close() + simulation_app.close() + return + + print(f"Using teleop device: {teleop_interface}") + + # reset environment + env.reset() + teleop_interface.reset() + + print("Teleoperation started. Press 'R' to reset the environment.") + + # simulate environment + while simulation_app.is_running(): + try: + # run everything in inference mode + with torch.inference_mode(): + # get device command + action = teleop_interface.advance() + + # Only apply teleop commands when active + if teleoperation_active: + # process actions + actions = action.repeat(env.num_envs, 1) + # apply actions + env.step(actions) + else: + env.sim.render() + + if should_reset_recording_instance: + env.reset() + should_reset_recording_instance = False + print("Environment reset complete") + except Exception as e: + omni.log.error(f"Error during simulation step: {e}") + break + + # close the simulator + env.close() + print("Environment closed") + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/environments/teleoperation/teleop_se3_agent.py b/scripts/environments/teleoperation/teleop_se3_agent.py index 021ee5ff80ff..993a0e61c992 100644 --- a/scripts/environments/teleoperation/teleop_se3_agent.py +++ b/scripts/environments/teleoperation/teleop_se3_agent.py @@ -21,7 +21,7 @@ default="keyboard", help="Device for interacting with environment. Examples: keyboard, spacemouse, gamepad, handtracking, manusvive", ) -parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument("--task", type=str, default="Dev-IK-Rel-Place-v0", help="Name of the task.") parser.add_argument("--sensitivity", type=float, default=1.0, help="Sensitivity factor.") parser.add_argument( "--enable_pinocchio", diff --git a/scripts/imitation_learning/robomimic/play_ensemble.py b/scripts/imitation_learning/robomimic/play_ensemble.py index fea3fdc51d5c..a0099677db37 100644 --- a/scripts/imitation_learning/robomimic/play_ensemble.py +++ b/scripts/imitation_learning/robomimic/play_ensemble.py @@ -261,7 +261,7 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, backup_controller = BackupController(env, device) state_guess = 0 last_state =0 - recovery_mode= False + recovery_mode= True state_guessed = False recovery_triggered = False object_knocked = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py index 2555e6d49938..7c168a609057 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py @@ -8,6 +8,9 @@ from isaaclab.utils import configclass from isaaclab.assets import RigidObjectCfg, ArticulationCfg from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.assets import AssetBaseCfg +from isaaclab.sim.spawners.from_files import UsdFileCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR from . import dev_env_cfg import math from isaaclab.managers import SceneEntityCfg @@ -29,7 +32,7 @@ def __post_init__(self): glassware = ChemistryGlassware() self.scene.stirplate = glassware.stirplate(pos=[0.5, 0.0, 0.01]) self.scene.scale = glassware.scale(pos=[0.3, -0.3, 0.01]) - + self.events.reset_object_position = EventTerm( func=mdp.reset_multiple_root_state_uniform, mode="reset", @@ -42,20 +45,20 @@ def __post_init__(self): ) self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace( prim_path="{ENV_REGEX_NS}/Robot", - init_state=ArticulationCfg.InitialStateCfg( - joint_pos={ + # init_state=ArticulationCfg.InitialStateCfg( + # joint_pos={ - "panda_joint1": 0.3281, - "panda_joint2": -0.3684, - "panda_joint3": -0.2787, - "panda_joint4": -2.6138, - "panda_joint5": -2.7527, - "panda_joint6": 2.4991, # +90° → keeps hand level - "panda_joint7": 0.3331, - "panda_finger_joint1": 0.04, # open gripper - "panda_finger_joint2": 0.04, - } - ), + # "panda_joint1": 0.3281, + # "panda_joint2": -0.3684, + # "panda_joint3": -0.2787, + # "panda_joint4": -2.6138, + # "panda_joint5": -2.7527, + # "panda_joint6": 2.4991, # +90° → keeps hand level + # "panda_joint7": 0.3331, + # "panda_finger_joint1": 0.04, # open gripper + # "panda_finger_joint2": 0.04, + # } + # ), ) # replace with relative position controller self.actions.arm_action = DifferentialInverseKinematicsActionCfg( diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py index 236c61480980..09c318a5104a 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py @@ -59,7 +59,9 @@ class ObjectTableSceneCfg(InteractiveSceneCfg): # Table table = AssetBaseCfg( prim_path="{ENV_REGEX_NS}/Table", + #init_state=AssetBaseCfg.InitialStateCfg(pos=[0, 0, 0], rot=[1.0, 0, 0, 0]), init_state=AssetBaseCfg.InitialStateCfg(pos=[0.5, 0, 0], rot=[0.707, 0, 0, 0.707]), + #spawn=UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/ThorlabsTable/table_instanceable.usd"), spawn=UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd"), ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py index 6e12df6cf39c..bd827fcac247 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py @@ -161,14 +161,14 @@ def is_object_lifted( def ee_frame_pos(env: ManagerBasedRLEnv, ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame")) -> torch.Tensor: ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] ee_frame_pos = ee_frame.data.target_pos_w[:, 0, :] - env.scene.env_origins[:, 0:3] - #print(f"EE pos : {ee_frame_pos}") + print(f"EE pos : {ee_frame_pos}") return ee_frame_pos def ee_frame_quat(env: ManagerBasedRLEnv, ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame")) -> torch.Tensor: ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] ee_frame_quat = ee_frame.data.target_quat_w[:, 0, :] - #print(f"EE pos : {ee_frame_quat}") + print(f"EE quat : {ee_frame_quat}") return ee_frame_quat From e0315169752b472e014eb716b1e2afeb882f0a3d Mon Sep 17 00:00:00 2001 From: LJones-alt Date: Fri, 9 Jan 2026 15:44:52 +0000 Subject: [PATCH 10/15] upgraded for difusion --- ...ng for Chemistry Laboratory Automation.pdf | Bin 0 -> 60316 bytes docker/.env.base | 2 +- .../bc_med_data_/20251217113929/config.json | 219 +++++++ .../bc_med_data_/20251217113936/config.json | 219 +++++++ .../bc_med_data_/20251217113940/config.json | 219 +++++++ .../bc_med_data_/20251217113940/config.json | 219 +++++++ .../bc_med_data_/20251217113940/config.json | 219 +++++++ .../bc_med_data_/20251217113941/config.json | 219 +++++++ 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.../config/franka/dev_ik_rel_env_place.py | 41 +- .../manipulation/cube_lift/lift_env_cfg.py | 7 +- .../cube_lift/mdp/observations.py | 18 +- .../cube_lift/mdp/terminations.py | 51 +- .../manipulation/stack_glassware/__init__.py | 9 + .../stack_glassware/config/__init__.py | 9 + .../stack_glassware/config/franka/__init__.py | 129 +++++ .../config/franka/agents/__init__.py | 4 + .../agents/robomimic/bc_rnn_image_84.json | 219 +++++++ .../agents/robomimic/bc_rnn_image_cosmos.json | 218 +++++++ .../agents/robomimic/bc_rnn_low_dim.json | 101 ++++ .../config/franka/bin_stack_ik_rel_env_cfg.py | 35 ++ .../franka/bin_stack_joint_pos_env_cfg.py | 207 +++++++ .../config/franka/stack_ik_abs_env_cfg.py | 59 ++ .../franka/stack_ik_rel_blueprint_env_cfg.py | 273 +++++++++ .../config/franka/stack_ik_rel_env_cfg.py | 69 +++ .../franka/stack_ik_rel_env_cfg_skillgen.py | 167 ++++++ ...stack_ik_rel_instance_randomize_env_cfg.py | 41 ++ .../stack_ik_rel_visuomotor_cosmos_env_cfg.py | 163 ++++++ .../franka/stack_ik_rel_visuomotor_env_cfg.py | 238 ++++++++ .../config/franka/stack_joint_pos_env_cfg.py | 168 ++++++ ...ck_joint_pos_instance_randomize_env_cfg.py | 204 +++++++ .../stack_glassware/config/galbot/__init__.py | 73 +++ .../config/galbot/stack_joint_pos_env_cfg.py | 278 +++++++++ .../config/galbot/stack_rmp_rel_env_cfg.py | 282 ++++++++++ .../config/ur10_gripper/__init__.py | 33 ++ .../ur10_gripper/stack_ik_rel_env_cfg.py | 80 +++ .../ur10_gripper/stack_joint_pos_env_cfg.py | 212 +++++++ .../stack_glassware/mdp/__init__.py | 11 + .../mdp/franka_stack_events.py | 314 +++++++++++ .../stack_glassware/mdp/observations.py | 532 ++++++++++++++++++ .../stack_glassware/mdp/terminations.py | 92 +++ .../stack_glassware/stack_env_cfg.py | 199 +++++++ .../stack_instance_randomize_env_cfg.py | 135 +++++ 99 files changed, 11830 insertions(+), 253 deletions(-) create mode 100644 Uncertainty Aware Imitation Learning for Chemistry Laboratory Automation.pdf create mode 100644 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zt8}D9s4ZUHs8beSVrd!gdvf8&j&4?5-=sy0z|w6dYsq|rwO!2`V$C`i8CfOK23wLtTMArsZ7-~#kOxT{h z$T1mxBgyneGSMU{)2HrSlM7twvUWnx#YFWpk+D{bB?4)zp}JnMc9$)=a*95D+@X?! zlqD?)NT$Vt&*6pw)wV%_Yz$QXWVj(g2Cuw9d0#Q41<02jgQ1Y1?DQB61L|du!7vz% zf;x#VN*a7O;E6}0G zu>-sUK#^`7J3#b|p~K= BackupSMWaitTime.REST: @@ -88,10 +100,10 @@ def infer_state_machine( # print("[SM] APPR above obj") pose_pos = wp.transform_get_translation(current_object_pose[tid]) pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) - - des_ee_pose[tid] = wp.transform(pose_pos, pose_rot) - des_ee_pose[tid] = wp.transform_multiply(offset[tid], des_ee_pose[tid]) - gripper_state[tid] = GripperState.CLOSE + # Go to a safe height ABOVE the object (z + 0.15m) with x offset + safe_above_pos = wp.vec3(pose_pos.x - 0.1, pose_pos.y, pose_pos.z + 0.15) + des_ee_pose[tid] = wp.transform(safe_above_pos, pose_rot) + gripper_state[tid] = GripperState.OPEN # Open gripper in preparation if distance_below_threshold( wp.transform_get_translation(ee_pose[tid]), wp.transform_get_translation(des_ee_pose[tid]), @@ -126,8 +138,8 @@ def infer_state_machine( # print("[SM] GRASP") pose_pos = wp.transform_get_translation(current_object_pose[tid]) pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) - # Apply offset in x-direction (5 cm = 0.05 m) - offset_pos = wp.vec3() #sample vial + 0.04 + # Stay at the approach position while closing gripper + offset_pos = wp.vec3(pose_pos.x - 0.1, pose_pos.y + 0.02, pose_pos.z + 0.02) des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) gripper_state[tid] = GripperState.CLOSE # wait for a while @@ -136,6 +148,7 @@ def infer_state_machine( print("[SM_INFO] : Moving from GRSP to LIFT_OBJECT") sm_state[tid] = BackupSM.LIFT_OBJECT sm_wait_time[tid] = 0.0 + elif state == BackupSM.LIFT_OBJECT: #print("[SM] LIFT") # uses starting position pose_pos = wp.transform_get_translation(object_pose[tid]) @@ -407,4 +420,4 @@ def compute(self, ee_pose: torch.Tensor, start_object_pose: torch.Tensor, curren # convert to torch #print(f'[SM DEBUG] new desired poition : {des_ee_pose}, gripper state {self.des_gripper_state}') return des_ee_pose , self.des_gripper_state, self.sm_state.clone().item() - #return torch.cat([des_ee_pose, self.des_gripper_state.unsqueeze(-1)], dim=-1) + #return torch.cat([des_ee_pose, self.des_gripper_state.unsqueeze(-1)], dim=-1) \ No newline at end of file diff --git a/scripts/imitation_learning/robomimic/play_ensemble.py b/scripts/imitation_learning/robomimic/play_ensemble.py index a0099677db37..a7ea7565e273 100644 --- a/scripts/imitation_learning/robomimic/play_ensemble.py +++ b/scripts/imitation_learning/robomimic/play_ensemble.py @@ -256,14 +256,13 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, recovery_cooldown_active = False recovery = [] recovery_steps = 0 + max_recovery_steps = 500 # Safety limit for recovery mode ##### CONFIG RECOVERY CONTROLLER #### backup_controller = BackupController(env, device) state_guess = 0 - last_state =0 - recovery_mode= True - state_guessed = False - recovery_triggered = False + last_state = 0 + recovery_mode = False object_knocked = False print(f'Running for horizon {horizon}') @@ -297,39 +296,62 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, metrics = ensemble_uncertainty(ensemble, obs) uncertainty = metrics['variance'] policy_actions = metrics['mean'] - # fix state guess - if sub_obs['grasp']: - if state_guess <4 : - state_guess = 4 + + # Robust state estimation based on actual observations + is_grasping = bool(sub_obs['grasp'].item()) if sub_obs['grasp'].numel() == 1 else bool(torch.any(sub_obs['grasp'])) + + # Update state guess based on actual gripper/object state + if is_grasping: + # Object is grasped - should be in LIFT or later states + if state_guess < 4: # LIFT_OBJECT = 4 + state_guess = 4 + print(f"[STATE] Updated to LIFT_OBJECT (grasp detected)") + else: + # Not grasping - if we thought we were lifting, we dropped it + if state_guess >= 4 and state_guess < 7: # Was in lift/move states + # Object dropped - need to re-approach + state_guess = 0 # Go back to REST to safely re-approach + print(f"[STATE] Reset to REST (grasp lost)") - sm_actions , state_guess = backup_controller.get_controller_action(state_guess, 0) + sm_actions, state_guess = backup_controller.get_controller_action(state_guess, 0) if last_state != state_guess: - #print(f"State {state_guess.item()}") + print(f"[STATE] Transition: {last_state} -> {state_guess}") last_state = state_guess - # this is the switching logic - if switchingLogic.check(uncertainty, i): + uncertainty_triggered = switchingLogic.check(uncertainty, i) + if uncertainty_triggered: # uncertainty triggered if use_recovery and not recovery_cooldown_active: # if we have switched to recovery - recovery_mode = True - recovery_activated_during_rollout+=1 - if not recovery_triggered : - print(f"[RECOVERY] Step {i}") - recovery_triggered = True + if not recovery_mode: + recovery_mode = True + recovery_activated_during_rollout+=1 + recovery_steps = 0 + print(f"[RECOVERY] Step {i} - Starting recovery from state {state_guess}") if recovery_mode: - # if holding the object, start with lifting actions = sm_actions - + recovery_steps += 1 + + # Exit recovery when task completes or after max steps + max_recovery_steps = 500 + if state_guess == 7 or recovery_steps > max_recovery_steps: # APPROACH_GOAL = 7 + print(f"[RECOVERY] Completed after {recovery_steps} steps, state={state_guess}") + recovery_mode = False + recovery_cooldown_active = True + recovery_cooldown_timer = recovery_cooldown_duration else: + # Decrement cooldown when not in recovery + if recovery_cooldown_active: + recovery_cooldown_timer -= 1 + if recovery_cooldown_timer <= 0: + recovery_cooldown_active = False + recovery_cooldown_timer = recovery_cooldown_duration + print("Recovery Cooldown Ended") + actions = policy_actions - if recovery_cooldown_timer <= 0: - recovery_cooldown_active = False - recovery_cooldown_timer = recovery_cooldown_duration - print("Recovery Cooldown Ended") # Unnormalize actions if args_cli.norm_factor_min is not None and args_cli.norm_factor_max is not None: @@ -396,153 +418,6 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, -def rollout_transformer(policy, env, success_term, horizon, device): - """Perform a single rollout of the policy in the environment, supporting sequence-based models.""" - policy.start_episode() - obs_dict, _ = env.reset() - traj = dict(actions=[], obs=[], next_obs=[], sub_obs=[], uncertainties=[]) - - context_length = getattr(policy, "n_obs_steps", 1) # works for transformer or rnn models - # print(f"Policy's expected sequence length (policy.n_obs_steps): {getattr(policy, 'n_obs_steps', 1)}") - - obs_seq = [] # list of previous obs for context window - - # Initial obs_dict from env.reset() - # print("Initial obs_dict shapes from env.reset():") - # for k, v in obs_dict["policy"].items(): - # print(f" policy['{k}']: shape={v.shape}, ndim={v.ndim}") - - # try: - # if hasattr(policy.policy, 'encoder') and hasattr(policy.policy.encoder, 'input_obs_group_shapes'): - # print("Policy's internal expected input_obs_group_shapes:") - # for group, keys in policy.policy.encoder.input_obs_group_shapes.items(): - # print(f" Group '{group}':") - # for k, shape in keys.items(): - # print(f" Key '{k}': shape={shape}, len(shape)={len(shape)}") - # else: - # print("Could not find policy.policy.encoder.input_obs_group_shapes.") - # except Exception as e: - # print(f"Error accessing policy.policy.encoder.input_obs_group_shapes: {e}") - - - for i in range(horizon): - # Prepare current observations from env for policy input - current_policy_obs = {} - for k, v in obs_dict["policy"].items(): - # Ensure low-dim observations are 1D (D,) - # If env returns (1, D), squeeze the batch dimension - if v.ndim > 1 and v.shape[0] == 1: - processed_v = v.squeeze(0).to(device) # Apply squeeze - # print(f" DEBUG: Squeezed '{k}' from {v.shape} to {processed_v.shape}") # Add debug print - else: - processed_v = v.to(device) # No squeeze needed, assume (D,) - current_policy_obs[k] = processed_v - - # # AFTER processing, print the shapes in current_policy_obs - # if i == 0: # Only print for the first step to avoid spamming - # print("current_policy_obs shapes AFTER squeezing (if applicable):") - # for k, v in current_policy_obs.items(): - # print(f" '{k}': shape={v.shape}, ndim={v.ndim}") - - - # Handle image observations specifically (if any) - if hasattr(env.cfg, "image_obs_list"): - for image_name in env.cfg.image_obs_list: - if image_name in obs_dict["policy"].keys(): - image = obs_dict["policy"][image_name].to(device) - # Assuming image comes as (H, W, C) and needs to be (C, H, W) - # If it's already (1, H, W, C), squeeze the batch dim first. - if image.ndim == 4 and image.shape[0] == 1: - image = image.squeeze(0) # Remove initial batch if present - - # Permute and normalize after ensuring no batch dim - image = image.permute(2, 0, 1).clone().float() # (C, H, W) - image = image / 255.0 - image = image.clip(0.0, 1.0) - current_policy_obs[image_name] = image - - - # Add subtask terms for logging/analysis, but they are not fed to the policy - # as per your trace (only "policy" observations are listed in the policy's encoder). - # You had prints for these before, assuming they are okay. - - # Append to context buffer. obs_seq should hold items with shape (D,) or (C, H, W) - traj["obs"].append(current_policy_obs) # Store the current, processed observation - obs_seq.append(current_policy_obs) # This is what forms the sequence input - - if len(obs_seq) > context_length: - obs_seq = obs_seq[-context_length:] - - # Pad if not enough context - if len(obs_seq) < context_length: - padding_obs = {} - for k, v in obs_seq[0].items(): # Use shape of first element in obs_seq for padding - # If obs_seq[0][k] is (D,) then zeros_like creates (D,) - # If obs_seq[0][k] is (C,H,W) then zeros_like creates (C,H,W) - padding_obs[k] = torch.zeros_like(v) - obs_seq = [padding_obs] * (context_length - len(obs_seq)) + obs_seq - - # Convert context list to batched sequence dict - seq_input = {} - for key in obs_seq[0]: - # Each step[key] here should be (D,) or (C, H, W) - # torch.stack will create (context_length, D) or (context_length, C, H, W) - # unsqueeze(0) will add the batch dimension: (1, context_length, D) or (1, context_length, C, H, W) - seq_input[key] = torch.stack([step[key] for step in obs_seq], dim=0).to(device=device) - #seq_input[key] = torch.stack([step[key] for step in obs_seq], dim=0).unsqueeze(0).to(device=device) - - # Print the final seq_input shapes just before policy call - # if i == 0: # Only print for the first step - # print("seq_input shapes for policy just before call:") - # for k, v in seq_input.items(): - # print(f" '{k}': shape={v.shape}, ndim={v.ndim}") - - # if i == 0: # Only print for the first step - # print("seq_input shapes for policy just before policy(seq_input) call (LAST CHECK):") - # for k, v in seq_input.items(): - # print(f" '{k}': shape={v.shape}, ndim={v.ndim}") - - # calculate uncertainty - # uncertainty_dict = mc_dropout_uncertainty_eval(policy=policy, obs=seq_input, niters=15) - # traj['uncertainties'].append(uncertainty_dict['variance']) - - # Compute action from sequence - actions = policy(seq_input) - - #print(f"DEBUG (Pre-Unnorm): Policy output 'actions' shape: {actions.shape}, ndim: {actions.ndim}, type: {type(actions)}") - - # Unnormalize actions - if args_cli.norm_factor_min is not None and args_cli.norm_factor_max is not None: - actions = ( - (actions + 1) * (args_cli.norm_factor_max - args_cli.norm_factor_min) - ) / 2 + args_cli.norm_factor_min - #print(f"DEBUG (Post-Unnorm): Policy output 'actions' shape: {actions.shape}, ndim: {actions.ndim}, type: {type(actions)}") - # Convert policy output (torch.Tensor) to numpy array for env.step() - # Assume actions comes as (1, Action_Dim) from policy, squeeze to (Action_Dim,) - if isinstance(actions, torch.Tensor): - # If it's a tensor, convert it to numpy, ensuring it's 1D (7,) - # We already know policy returns (7,) so no squeeze is needed *here*. - actions = actions.cpu().numpy() - - actions_tensor = torch.from_numpy(actions).to(device=device).float() - actions_tensor = actions_tensor.unsqueeze(0) - # Apply actions - #obs_dict, _, terminated, truncated, _ = env.step(actions) - obs_dict, _, terminated, truncated, _ = env.step(actions_tensor) - # Record trajectory - traj["next_obs"] should append the raw observation dictionary from env.step(). - traj["actions"].append(actions.tolist()) - traj["next_obs"].append(obs_dict["policy"]) - - - # Check if rollout was successful - if bool(success_term.func(env, **success_term.params)[0]): - return True, traj - elif terminated or truncated: - return False, traj - - return False, traj - - def load_ensemble(device, ensemble_path): with open(ensemble_path, 'r') as file: @@ -614,40 +489,40 @@ def main(): # Lets set these to the 0.99 confidence parameters = { 'beaker_lift' :{ - 0 : { - "confidence_level": 0.010360572377319478, - "window_size": 9, - "max_peaks": 9 + 0 : { + "confidence_level": 0.018646507043923632, + "window_size": 15, + "max_peaks": 10 }, 1 : { - "confidence_level": 0.012117819935441015, - "window_size": 9, + "confidence_level": 0.018898154803458085, + "window_size": 10, "max_peaks": 9 }, 2 : { - "confidence_level": 0.0114854090040122, - "window_size": 9, - "max_peaks": 9 + "confidence_level": 0.017589774107725654, + "window_size": 15, + "max_peaks": 7 }, 3 : { - "confidence_level": 0.01721086513038985, - "window_size": 9, - "max_peaks": 9 + "confidence_level": 0.02829341592326851, + "window_size": 12, + "max_peaks": 8 }, 4 : { - "confidence_level": 0.01516669949421836, - "window_size": 9, - "max_peaks": 9 + "confidence_level": 0.029170220902127186, + "window_size": 15, + "max_peaks": 11 }, 5 : { - "confidence_level": 0.011445348395548671, - "window_size": 9, - "max_peaks": 9 + "confidence_level": 0.017449474558650185, + "window_size": 12, + "max_peaks": 7 }, 6 : { - "confidence_level": 0.0001330413469565007, - "window_size": 15, - "max_peaks": 9 + "confidence_level": 0.6324478323150455, + "window_size": 14, + "max_peaks": 14 }, }, @@ -689,6 +564,7 @@ def main(): failed_no_intervention =[] intervention = [] intervention_success = [] + knocked=0 intervention_failure = [] for trial in range(args_cli.num_rollouts): print(f"[INFO] Starting trial {trial}") @@ -749,15 +625,19 @@ def main(): intervention_failure.append(1) else: intervention.append(0) - print(f'Current rate {results.count(True)/len(results)}') - print(f"[INFO] Trial {trial}: {terminated}\n") + if "knocked" in traj['failure'] or (len(traj['failure']) > 0 and traj['failure'][-1] == "knocked"): + knocked+=1 + + print(f'Current success rate: {results.count(True)/len(results):.2%}') + print(f'Current intervention rate: {sum(intervention)/len(intervention):.2%} ({sum(intervention)}/{len(intervention)})') + print(f"[INFO] Trial {trial}: {'SUCCESS' if terminated else 'FAILED'}\n") #loghelper.stopEpoch(trial) print(f"\nSuccessful trials: {results.count(True)}, out of {len(results)} trials") print(f"Success rate: {results.count(True) / len(results)}") print(f'Failed runs with no intervention : {sum(failed_no_intervention)}') print(f"Intervention rate : {sum(intervention)/len(intervention)}. Of which success : {sum(intervention_success)}, of which failed {sum(intervention_failure)}") - + print(f"Number of times object knocked over : {knocked}") print(f"Trial Results: {results}\n") diff --git a/scripts/imitation_learning/robomimic/train_ensemble.py b/scripts/imitation_learning/robomimic/train_ensemble.py index 744753d669b1..047c62daece7 100644 --- a/scripts/imitation_learning/robomimic/train_ensemble.py +++ b/scripts/imitation_learning/robomimic/train_ensemble.py @@ -140,7 +140,7 @@ def normalize_hdf5_actions(config: Config, log_dir: str) -> str: # -def train(config: Config, device: str, log_dirs: list[str], ckpt_dirs: list[str], video_dirs: list[str], use_config_seed: bool = True, ensemble_size: int = 1): +def train(config: Config, device: str, log_dirs: list[str], ckpt_dirs: list[str], video_dirs: list[str], use_config_seed: bool = True, ensemble_size: int = 1, use_bootstrap: bool = False): """Train an ensemble of models using the algorithm specified in config. If ensemble_size is not specified, a single model will be trained. Args: @@ -149,6 +149,9 @@ def train(config: Config, device: str, log_dirs: list[str], ckpt_dirs: list[str] log_dirs: List of directories to save logs for each network in the ensemble. ckpt_dirs: List of directories to save checkpoints for each network in the ensemble. video_dirs: List of directories to save videos for each network in the ensemble. + use_config_seed: Whether to use config seed. + ensemble_size: Number of models in the ensemble. + use_bootstrap: If True, each model trains on a bootstrap sample (with replacement). """ # first set seeds if use_config_seed: @@ -262,6 +265,32 @@ def train(config: Config, device: str, log_dirs: list[str], ckpt_dirs: list[str] #config.algo.transformer.num_layers = 1 #config.algo.transformer.num_heads = 1 for model_num in range(ensemble_size): + # Set different seed for each model to ensure diversity + model_seed = config.train.seed + model_num + np.random.seed(model_seed) + torch.manual_seed(model_seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(model_seed) + print(f"\n============= Training Model {model_num} with Seed {model_seed} =============") + + # Create bootstrap sample if enabled (each model sees different data) + if use_bootstrap: + print(f"Creating bootstrap sample for model {model_num}...") + bootstrap_trainset = create_bootstrap_sample(trainset, seed=model_seed) + if isinstance(bootstrap_trainset, Subset): + bootstrap_sampler = bootstrap_trainset.dataset.get_dataset_sampler() if hasattr(bootstrap_trainset.dataset, 'get_dataset_sampler') else None + else: + bootstrap_sampler = bootstrap_trainset.get_dataset_sampler() if hasattr(bootstrap_trainset, 'get_dataset_sampler') else None + + train_loader = DataLoader( + dataset=bootstrap_trainset, + sampler=bootstrap_sampler, + batch_size=config.train.batch_size, + shuffle=(bootstrap_sampler is None), + num_workers=config.train.num_data_workers, + drop_last=True, + ) + # setup a wand run wand_experiment_name = f"{config.experiment.name}-model_{model_num}" run=wandb.init( @@ -375,7 +404,7 @@ def train(config: Config, device: str, log_dirs: list[str], ckpt_dirs: list[str] run.finish() #config.algo.transformer.num_layers = config.algo.transformer.num_layers + 1 #config.algo.transformer.num_heads = config.algo.transformer.num_heads + 1 - # run.finish() + #run.finish() # terminate logging data_logger.close() @@ -398,6 +427,34 @@ def keep_data_percentage(trainset, percentage): return trainset +def create_bootstrap_sample(trainset, seed: int): + """Create a bootstrap sample (sampling with replacement) from the training set. + + Each bootstrap sample contains N samples drawn with replacement from the original + dataset of size N. On average, ~63.2% of samples will be unique, with ~36.8% duplicates. + This creates diversity across ensemble members. + + Args: + trainset: The original training dataset. + seed: Random seed for reproducibility. + + Returns: + A Subset of the training data with bootstrap sampling. + """ + np.random.seed(seed) + total_size = len(trainset) + # Sample WITH replacement - same sample can appear multiple times + indices = np.random.choice(total_size, size=total_size, replace=True) + + # Count unique samples + unique_count = len(np.unique(indices)) + unique_percent = (unique_count / total_size) * 100 + + print(f"Bootstrap sample created: {unique_count}/{total_size} unique samples ({unique_percent:.1f}%)") + + return Subset(trainset, indices) + + def main(args: argparse.Namespace): """Train a model on a task using a specified algorithm. @@ -455,6 +512,7 @@ def main(args: argparse.Namespace): log_dirs, ckpt_dirs, video_dirs = [], [], [] for i in range(args.ensemble_size): + new_output_dir = f"{original_output_dir}/model{i}/" config.train.output_dir = new_output_dir log_dir, ckpt_dir, video_dir = TrainUtils.get_exp_dir(config) @@ -485,7 +543,8 @@ def main(args: argparse.Namespace): device=device, log_dirs=log_dirs, ckpt_dirs=ckpt_dirs, video_dirs=video_dirs, use_config_seed=True, - ensemble_size=args.ensemble_size + ensemble_size=args.ensemble_size, + use_bootstrap=args.use_bootstrap ) except Exception as e: @@ -520,6 +579,8 @@ def main(args: argparse.Namespace): parser.add_argument("--dataset_percentage", type=float, default=1.0, help="Percentage of samples from the dataset to use") parser.add_argument("--ensemble_size", type=int, default=15, help="Defines the number of networks in the ensemble.") + parser.add_argument("--use_bootstrap", action="store_true", default=False, + help="Use bootstrap sampling (with replacement) for each ensemble member. Creates more diversity.") args = parser.parse_args() diff --git a/source/isaaclab/isaaclab/envs/mdp/events.py b/source/isaaclab/isaaclab/envs/mdp/events.py index 5146e94daa8c..f9ffd47ba874 100644 --- a/source/isaaclab/isaaclab/envs/mdp/events.py +++ b/source/isaaclab/isaaclab/envs/mdp/events.py @@ -1099,7 +1099,8 @@ def reset_multiple_root_state_uniform( pose_range: dict[str, tuple[float, float]], velocity_range: dict[str, tuple[float, float]], asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), - asset2_cfg: SceneEntityCfg=SceneEntityCfg("stirplate") + asset2_cfg: SceneEntityCfg=SceneEntityCfg("stirplate"), + # asset3_cfg: SceneEntityCfg=SceneEntityCfg("scale") ): """Reset the asset root state to a random position and velocity uniformly within the given ranges. @@ -1117,9 +1118,12 @@ def reset_multiple_root_state_uniform( # extract the used quantities (to enable type-hinting) asset: RigidObject | Articulation = env.scene[asset_cfg.name] asset2: RigidObject | Articulation = env.scene[asset2_cfg.name] + # asset3: RigidObject | Articulation = env.scene[asset3_cfg.name] + # get default root state root_states = asset.data.default_root_state[env_ids].clone() root_states2 = asset2.data.default_root_state[env_ids].clone() + #root_states3 = asset3.data.default_root_state[env_ids].clone() # poses range_list = [pose_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z", "roll", "pitch", "yaw"]] ranges = torch.tensor(range_list, device=asset.device) @@ -1128,12 +1132,78 @@ def reset_multiple_root_state_uniform( positions = root_states[:, 0:3] + env.scene.env_origins[env_ids] + rand_samples[:, 0:3] #print(f"positions : ", positions) - + # goal_pose = root_states3[:, 0:3] + env.scene.env_origins[env_ids] + rand_samples[:, 0:3] + orientations_delta = math_utils.quat_from_euler_xyz(rand_samples[:, 3], rand_samples[:, 4], rand_samples[:, 5]) orientations = math_utils.quat_mul(root_states[:, 3:7], orientations_delta) orientations2_delta = math_utils.quat_from_euler_xyz(rand_samples[:, 3], rand_samples[:, 4], rand_samples[:, 5]) orientations2 = math_utils.quat_mul(root_states2[:, 3:7], orientations_delta) + # orientations3_delta = math_utils.quat_from_euler_xyz(rand_samples[:, 3], rand_samples[:, 4], rand_samples[:, 5]) + #orientations3 = math_utils.quat_mul(root_states3[:, 3:7], orientations_delta) + # velocities + range_list = [velocity_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z", "roll", "pitch", "yaw"]] + ranges = torch.tensor(range_list, device=asset.device) + rand_samples = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (len(env_ids), 6), device=asset.device) + + velocities = root_states[:, 7:13] + rand_samples + positions[:,2] += 0.06 + # set into the physics simulation + asset.write_root_pose_to_sim(torch.cat([positions, orientations], dim=-1), env_ids=env_ids) + asset.write_root_velocity_to_sim(velocities, env_ids=env_ids) + # remote iks plate offset + positions[:,0] += 0.04 + positions[:,2] -= 0.06 + asset2.write_root_pose_to_sim(torch.cat([positions, orientations2], dim=-1), env_ids=env_ids) + asset2.write_root_velocity_to_sim(velocities, env_ids=env_ids) + # asset3.write_root_pose_to_sim(torch.cat([goal_pose, orientations3], dim=-1), env_ids=env_ids) + +def reset_place_root_state_uniform( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + pose_range: dict[str, tuple[float, float]], + velocity_range: dict[str, tuple[float, float]], + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + asset2_cfg: SceneEntityCfg=SceneEntityCfg("stirplate"), + asset3_cfg: SceneEntityCfg=SceneEntityCfg("scale") +): + """Reset the asset root state to a random position and velocity uniformly within the given ranges. + + This function randomizes the root position and velocity of the asset. + + * It samples the root position from the given ranges and adds them to the default root position, before setting + them into the physics simulation. + * It samples the root orientation from the given ranges and sets them into the physics simulation. + * It samples the root velocity from the given ranges and sets them into the physics simulation. + + The function takes a dictionary of pose and velocity ranges for each axis and rotation. The keys of the + dictionary are ``x``, ``y``, ``z``, ``roll``, ``pitch``, and ``yaw``. The values are tuples of the form + ``(min, max)``. If the dictionary does not contain a key, the position or velocity is set to zero for that axis. + """ + # extract the used quantities (to enable type-hinting) + asset: RigidObject | Articulation = env.scene[asset_cfg.name] + asset2: RigidObject | Articulation = env.scene[asset2_cfg.name] + asset3: RigidObject | Articulation = env.scene[asset3_cfg.name] + # get default root state + root_states = asset.data.default_root_state[env_ids].clone() + root_states2 = asset2.data.default_root_state[env_ids].clone() + root_states3 = asset3.data.default_root_state[env_ids].clone() + # poses + range_list = [pose_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z", "roll", "pitch", "yaw"]] + ranges = torch.tensor(range_list, device=asset.device) + #print(f"ranges : {ranges}") + rand_samples = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (len(env_ids), 6), device=asset.device) + + positions = root_states[:, 0:3] + env.scene.env_origins[env_ids] + rand_samples[:, 0:3] + #print(f"positions : ", positions) + goal_pose = root_states3[:, 0:3] + env.scene.env_origins[env_ids] + rand_samples[:, 0:3] + + orientations_delta = math_utils.quat_from_euler_xyz(rand_samples[:, 3], rand_samples[:, 4], rand_samples[:, 5]) + orientations = math_utils.quat_mul(root_states[:, 3:7], orientations_delta) + orientations2_delta = math_utils.quat_from_euler_xyz(rand_samples[:, 3], rand_samples[:, 4], rand_samples[:, 5]) + orientations2 = math_utils.quat_mul(root_states2[:, 3:7], orientations_delta) + orientations3_delta = math_utils.quat_from_euler_xyz(rand_samples[:, 3], rand_samples[:, 4], rand_samples[:, 5]) + orientations3 = math_utils.quat_mul(root_states3[:, 3:7], orientations_delta) # velocities range_list = [velocity_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z", "roll", "pitch", "yaw"]] ranges = torch.tensor(range_list, device=asset.device) @@ -1149,6 +1219,8 @@ def reset_multiple_root_state_uniform( positions[:,2] -= 0.06 asset2.write_root_pose_to_sim(torch.cat([positions, orientations2], dim=-1), env_ids=env_ids) asset2.write_root_velocity_to_sim(velocities, env_ids=env_ids) + asset3.write_root_pose_to_sim(torch.cat([goal_pose, orientations3], dim=-1), env_ids=env_ids) + def reset_root_state_with_random_orientation( env: ManagerBasedEnv, diff --git a/source/isaaclab/isaaclab/safety/switchingLogic.py b/source/isaaclab/isaaclab/safety/switchingLogic.py index 86dcc7e5030a..bfcd265adbdf 100644 --- a/source/isaaclab/isaaclab/safety/switchingLogic.py +++ b/source/isaaclab/isaaclab/safety/switchingLogic.py @@ -30,18 +30,19 @@ def halfway_check(self, step:int=0, grasp_state:bool=True): return True return False - +s def check(self, uncertainties, step) -> bool: # 1. Update windows for joint_num, unc in enumerate(uncertainties): # Check for bounds if uncertainties is smaller than self.joints self.traj[joint_num][step]=unc.item() + #print(f'Action comp {joint_num} uncertainty at step {step}: {unc.item()}') if step > self.params[joint_num]['window_size']: test_window = self.traj[joint_num][step-self.params[joint_num]['window_size']:step] peaks = sum(1 for x in test_window if x>= self.params[joint_num]['confidence_level']) - # print(f'joint {joint_num}, window: {test_window}') - # print(f"manually found peaks : {peaks}") + #print(f'action {joint_num}, window: {test_window}') + # print(f"Action comp {joint_num}, {unc.item()} limit {self.params[joint_num]['confidence_level']} manually found peaks : {peaks}") if peaks>=self.params[joint_num]['max_peaks']: if not self.triggered: print(f"Uncertaity in joint {joint_num} triggered") diff --git a/source/isaaclab_assets/isaaclab_assets/robots/franka.py b/source/isaaclab_assets/isaaclab_assets/robots/franka.py index 36d07253425b..1a8396520ca5 100644 --- a/source/isaaclab_assets/isaaclab_assets/robots/franka.py +++ b/source/isaaclab_assets/isaaclab_assets/robots/franka.py @@ -69,7 +69,7 @@ damping=1e2, ), }, - soft_joint_pos_limit_factor=1.0, + soft_joint_pos_limit_factor=0.9, ) """Configuration of Franka Emika Panda robot.""" diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_blueprint_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_blueprint_mimic_env_cfg.py index b73ed476a072..0d3e07572fd7 100755 --- a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_blueprint_mimic_env_cfg.py +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_blueprint_mimic_env_cfg.py @@ -6,7 +6,7 @@ from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig from isaaclab.utils import configclass -from isaaclab_tasks.manager_based.manipulation.cube_lift.config.franka.dev_ik_rel_env_obs import FrankaDevEnvCfg +from isaaclab_tasks.manager_based.manipulation.cube_lift.config.franka.dev_ik_rel_env_place import FrankaDevEnvCfg @configclass diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py index cf0eb52fdc4f..20181d3aa4a2 100755 --- a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py @@ -6,7 +6,7 @@ from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig from isaaclab.utils import configclass -from isaaclab_tasks.manager_based.manipulation.cube_lift.config.franka.dev_ik_rel_env_obs import FrankaDevEnvCfg +from isaaclab_tasks.manager_based.manipulation.cube_lift.config.franka.dev_ik_rel_env_place import FrankaDevEnvCfg @configclass @@ -30,6 +30,7 @@ def __post_init__(self): self.datagen_config.generation_select_src_per_subtask = True self.datagen_config.generation_transform_first_robot_pose = False self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.generation_relative = True self.datagen_config.max_num_failures = 100 self.datagen_config.seed = 101 @@ -67,7 +68,7 @@ def __post_init__(self): # Corresponding key for the binary indicator in "datagen_info" for completion subtask_term_signal="grasp", # Time offsets for data generation when splitting a trajectory - subtask_term_offset_range=(0, 0), + subtask_term_offset_range=(10, 20), # Selection strategy for source subtask segment selection_strategy="nearest_neighbor_object", # Optional parameters for the selection strategy function @@ -75,7 +76,7 @@ def __post_init__(self): # Amount of action noise to apply during this subtask action_noise=0.03, # Number of interpolation steps to bridge to this subtask segment - num_interpolation_steps=0, + num_interpolation_steps=5, # Additional fixed steps for the robot to reach the necessary pose num_fixed_steps=0, # If True, apply action noise during the interpolation phase and execution @@ -110,9 +111,33 @@ def __post_init__(self): # Each subtask involves manipulation with respect to a single object frame. object_ref="object", # End of final subtask does not need to be detected - # LOL YOU DO + # This is actually the lift subtask_term_signal="appr_goal", # No time offsets for the final subtask + subtask_term_offset_range=(10, 20), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_robot_distance", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) + + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # End of final subtask - when using --annotate_subtask_start_signals, + # the last subtask must also have a signal name + subtask_term_signal="success", + # No time offsets for the final subtask subtask_term_offset_range=(0, 0), # Selection strategy for source subtask segment selection_strategy="nearest_neighbor_robot_distance", @@ -121,12 +146,14 @@ def __post_init__(self): # Amount of action noise to apply during this subtask action_noise=0.03, # Number of interpolation steps to bridge to this subtask segment - num_interpolation_steps=0, + num_interpolation_steps=5, # Additional fixed steps for the robot to reach the necessary pose num_fixed_steps=0, # If True, apply action noise during the interpolation phase and execution apply_noise_during_interpolation=False, ) ) + + self.subtask_configs["franka"] = subtask_configs \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/__init__.py index 14d95703522d..39a8d4c281c6 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/__init__.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/__init__.py @@ -54,6 +54,7 @@ "robomimic_bc_trans_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_trans.json"), "robomimic_hbc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/hbc.json"), "robomimic_bcq_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bcq.json"), + "robomimic_diffusion_policy_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/diffusion_policy.json"), #"robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc.json"), }, disable_env_checker=True, diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/diffusion_policy.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/diffusion_policy.json new file mode 100644 index 000000000000..47fafeea8cb1 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/diffusion_policy.json @@ -0,0 +1,162 @@ +{ + "algo_name": "diffusion_policy", + "experiment": { + "name": "diffusion_policy", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "Lift_beaker_diffusion" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 50, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": false + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": null, + "additional_envs": null, + "render": false, + "render_video": false, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "terminate_on_success": true, + "n": 50, + "horizon": 400, + "rate": 50 + } + }, + "train": { + "data": null, + "output_dir": "/training_runs/diffusion_policy", + "num_data_workers": 4, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_normalize_obs": false, + "hdf5_filter_key": "train", + "hdf5_validation_filter_key": "valid", + "seq_length": 10, + "frame_stack": 1, + "pad_frame_stack": true, + "pad_seq_length": true, + "get_pad_mask": false, + "goal_mode": null, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "action_keys": [ + "actions" + ], + "cuda": true, + "batch_size": 256, + "num_epochs": 3000, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adamw", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 1.0, + "epoch_schedule": [], + "scheduler_type": "constant_with_warmup" + }, + "regularization": { + "L2": 0.0001 + } + } + }, + "horizon": { + "observation_horizon": 2, + "action_horizon": 8, + "prediction_horizon": 16 + }, + "unet": { + "enabled": true, + "diffusion_step_embed_dim": 256, + "down_dims": [256, 512, 1024], + "kernel_size": 5, + "n_groups": 8 + }, + "ema": { + "enabled": true, + "power": 0.75 + }, + "ddpm": { + "enabled": true, + "num_train_timesteps": 100, + "num_inference_timesteps": 100, + "beta_schedule": "squaredcos_cap_v2", + "clip_sample": true, + "prediction_type": "epsilon" + }, + "ddim": { + "enabled": false, + "num_train_timesteps": 100, + "num_inference_timesteps": 10, + "beta_schedule": "squaredcos_cap_v2", + "clip_sample": true, + "set_alpha_to_one": true, + "steps_offset": 0, + "prediction_type": "epsilon" + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object_position", + "target_object_position", + "joint_pos", + "joint_vel", + "actions" + ], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0 + } + }, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + } +} diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py index 16d4e657c655..7f7b0e7bae8d 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py @@ -121,6 +121,7 @@ "robomimic_bc_trans_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_trans.json"), "robomimic_hbc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/hbc.json"), "robomimic_bcq_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bcq.json"), + "robomimic_diffusion_policy_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/diffusion_policy.json"), #"robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc.json"), }, disable_env_checker=True, @@ -135,6 +136,7 @@ "robomimic_bc_trans_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_trans.json"), "robomimic_hbc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/hbc.json"), "robomimic_bcq_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bcq.json"), + "robomimic_diffusion_policy_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/diffusion_policy.json"), }, disable_env_checker=True, @@ -150,6 +152,7 @@ "robomimic_bc_trans_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_trans.json"), "robomimic_hbc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/hbc.json"), "robomimic_bcq_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bcq.json"), + "robomimic_diffusion_policy_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/diffusion_policy.json"), }, disable_env_checker=True, diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json index db8907778f4b..d5071d660c5d 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json @@ -7,7 +7,7 @@ "terminal_output_to_txt": true, "log_tb": true, "log_wandb": true, - "wandb_proj_name": "Lift_beaker_new_demos_med_qual" + "wandb_proj_name": "pick_place_new_demos_med_qual" }, "save": { "enabled": true, @@ -36,14 +36,14 @@ }, "train": { "data": null, - "output_dir": "docs/training_runs/bc_rnn", + "output_dir": "/workspace/isaaclab/docs/training_runs/bc_rnn", "num_data_workers": 4, "hdf5_cache_mode": "low_dim", "hdf5_use_swmr": true, "hdf5_normalize_obs": false, "hdf5_filter_key": "train", "hdf5_validation_filter_key": "valid", - "seq_length": 10, + "seq_length": 100, "dataset_keys": [ "actions", "rewards", @@ -53,7 +53,7 @@ "goal_mode": null, "cuda": true, "batch_size": 100, - "num_epochs": 2000, + "num_epochs": 1500, "seed": 101 }, "algo": { @@ -87,7 +87,7 @@ "rnn": { "enabled": true, - "horizon": 10, + "horizon": 100, "hidden_dim": 400, "rnn_type": "LSTM", "num_layers": 2, diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/diffusion_policy.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/diffusion_policy.json new file mode 100644 index 000000000000..ae7cffd9ea58 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/diffusion_policy.json @@ -0,0 +1,162 @@ +{ + "algo_name": "diffusion_policy", + "experiment": { + "name": "diffusion_policy_place", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "pick_place_diffusion" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 50, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": false + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": "Dev-IK-Rel-Place-v0", + "additional_envs": null, + "render": false, + "render_video": false, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "terminate_on_success": true, + "n": 50, + "horizon": 400, + "rate": 50 + } + }, + "train": { + "data": null, + "output_dir": "/training_runs/diffusion_policy", + "num_data_workers": 4, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_normalize_obs": false, + "hdf5_filter_key": "train", + "hdf5_validation_filter_key": "valid", + "seq_length": 10, + "frame_stack": 1, + "pad_frame_stack": true, + "pad_seq_length": true, + "get_pad_mask": false, + "goal_mode": null, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "action_keys": [ + "actions" + ], + "cuda": true, + "batch_size": 256, + "num_epochs": 3000, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adamw", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 1.0, + "epoch_schedule": [], + "scheduler_type": "constant_with_warmup" + }, + "regularization": { + "L2": 0.0001 + } + } + }, + "horizon": { + "observation_horizon": 2, + "action_horizon": 8, + "prediction_horizon": 16 + }, + "unet": { + "enabled": true, + "diffusion_step_embed_dim": 256, + "down_dims": [256, 512, 1024], + "kernel_size": 5, + "n_groups": 8 + }, + "ema": { + "enabled": true, + "power": 0.75 + }, + "ddpm": { + "enabled": true, + "num_train_timesteps": 100, + "num_inference_timesteps": 100, + "beta_schedule": "squaredcos_cap_v2", + "clip_sample": true, + "prediction_type": "epsilon" + }, + "ddim": { + "enabled": false, + "num_train_timesteps": 100, + "num_inference_timesteps": 10, + "beta_schedule": "squaredcos_cap_v2", + "clip_sample": true, + "set_alpha_to_one": true, + "steps_offset": 0, + "prediction_type": "epsilon" + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object_position", + "target_object_position", + "joint_pos", + "joint_vel", + "actions" + ], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0 + } + }, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + } +} diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py index 7c168a609057..fd754ccfbc58 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py @@ -7,19 +7,23 @@ from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg from isaaclab.utils import configclass from isaaclab.assets import RigidObjectCfg, ArticulationCfg +from isaaclab.managers import ObservationTermCfg as ObsTerm from isaaclab.managers import EventTermCfg as EventTerm from isaaclab.assets import AssetBaseCfg from isaaclab.sim.spawners.from_files import UsdFileCfg from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from source.isaaclab_assets.isaaclab_assets.robots.universal_robots import UR10_CFG from . import dev_env_cfg import math from isaaclab.managers import SceneEntityCfg from isaaclab_assets.glassware.glassware import ChemistryGlassware from isaaclab_tasks.manager_based.manipulation.cube_lift import mdp +from isaaclab.managers import TerminationTermCfg as DoneTerm ## # Pre-defined configs ## from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip +from isaaclab_assets.robots.universal_robots import UR10e_ROBOTIQ_GRIPPER_CFG @@ -34,31 +38,34 @@ def __post_init__(self): self.scene.scale = glassware.scale(pos=[0.3, -0.3, 0.01]) self.events.reset_object_position = EventTerm( - func=mdp.reset_multiple_root_state_uniform, + func=mdp.reset_place_root_state_uniform, mode="reset", params={ "pose_range": {"x": (0, 0.2), "y": (0, 0.25), "z": (0.02, 0.02)}, "velocity_range": {}, "asset_cfg": SceneEntityCfg("object", body_names="Object"), - "asset2_cfg" : SceneEntityCfg("stirplate") + "asset2_cfg" : SceneEntityCfg("stirplate"), + "asset3_cfg" : SceneEntityCfg("scale") + }, ) + #self.scene.robot = UR10e_ROBOTIQ_GRIPPER_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace( prim_path="{ENV_REGEX_NS}/Robot", - # init_state=ArticulationCfg.InitialStateCfg( - # joint_pos={ + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ - # "panda_joint1": 0.3281, - # "panda_joint2": -0.3684, - # "panda_joint3": -0.2787, - # "panda_joint4": -2.6138, - # "panda_joint5": -2.7527, - # "panda_joint6": 2.4991, # +90° → keeps hand level - # "panda_joint7": 0.3331, - # "panda_finger_joint1": 0.04, # open gripper - # "panda_finger_joint2": 0.04, - # } - # ), + "panda_joint1": 0.3281, + "panda_joint2": -0.3684, + "panda_joint3": -0.2787, + "panda_joint4": -2.6138, + "panda_joint5": -2.7527, + "panda_joint6": 2.4991, # +90° → keeps hand level + "panda_joint7": 0.3331, + "panda_finger_joint1": 0.04, # open gripper + "panda_finger_joint2": 0.04, + } + ), ) # replace with relative position controller self.actions.arm_action = DifferentialInverseKinematicsActionCfg( @@ -69,8 +76,10 @@ def __post_init__(self): scale=0.5, body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), ) + self.terminations.success=DoneTerm(func=mdp.object_stacked_upright, params={"lower_object_cfg": SceneEntityCfg("scale")}) - + self.observations.subtask_terms.appr_goal=ObsTerm(func=mdp.is_object_lifted, params={"threshold":0.15} + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py index 09c318a5104a..eb2138fa5ddf 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py @@ -136,7 +136,8 @@ class PolicyCfg(ObsGroup): joint_pos = ObsTerm(func=mdp.joint_pos_rel) joint_vel = ObsTerm(func=mdp.joint_vel_rel) object_position = ObsTerm(func=mdp.object_position_in_robot_root_frame) - target_object_position = ObsTerm(func=mdp.generated_commands, params={"command_name": "object_pose"}) + #target_object_position = ObsTerm(func=mdp.generated_commands, params={"command_name": "object_pose"}) + target_object_position = ObsTerm(func=mdp.target_position, params={"object_cfg": SceneEntityCfg("scale")}) actions = ObsTerm(func=mdp.last_action) # object_to_target = ObsTerm(func=mdp.object_near_goal) eef_pos = ObsTerm(func=mdp.ee_frame_pos) @@ -242,7 +243,7 @@ class EventCfg: # "asset_cfg": SceneEntityCfg("object", body_names="Object"), # }, params={ - "scale_range": {"x": (0.5, 0.5), "y": (0.5, 0.5), "z": (0.6, 0.7)}, + "scale_range": {"x": (0.5, 0.5), "y": (0.5, 0.5), "z": (0.6, 0.6)}, "asset_cfg": SceneEntityCfg("object", body_names="Object"), }, # params={ @@ -353,7 +354,7 @@ def __post_init__(self): # general settings self.decimation = 2 - self.episode_length_s = 60 + self.episode_length_s = 500 # simulation settings self.sim.dt = 0.01 # 100Hz self.sim.render_interval = self.decimation diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py index bd827fcac247..75355c70d87a 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py @@ -151,6 +151,7 @@ def is_object_lifted( ): #return true when object z coord above a threshold value object = env.scene[obj_cfg.name] + print(f"Object z pos : {object.data.root_pos_w[:, 2]}") # if object.data.root_pos_w[:, 2].item() > threshold : # print(f"Observed Object Lifted : {object.data.root_pos_w[:, 2].item()}") # loghelper.logsubtask(LogType.LIFT) @@ -161,14 +162,14 @@ def is_object_lifted( def ee_frame_pos(env: ManagerBasedRLEnv, ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame")) -> torch.Tensor: ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] ee_frame_pos = ee_frame.data.target_pos_w[:, 0, :] - env.scene.env_origins[:, 0:3] - print(f"EE pos : {ee_frame_pos}") + #print(f"EE pos : {ee_frame_pos}") return ee_frame_pos def ee_frame_quat(env: ManagerBasedRLEnv, ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame")) -> torch.Tensor: ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] ee_frame_quat = ee_frame.data.target_quat_w[:, 0, :] - print(f"EE quat : {ee_frame_quat}") + #print(f"EE quat : {ee_frame_quat}") return ee_frame_quat @@ -306,4 +307,15 @@ def object_knocked(env: ManagerBasedRLEnv, upper_object_cfg: SceneEntityCfg = Sc if tilt_deg.cpu().detach().numpy()[0] > max_tilt: # print("object knocked") return torch.tensor([True], device='cuda:0') - else :return torch.tensor([False], device='cuda:0') \ No newline at end of file + else :return torch.tensor([False], device='cuda:0') + +def target_position( + env: ManagerBasedRLEnv, + object_cfg: SceneEntityCfg = SceneEntityCfg("scale"), + # loghelper : LoggingHelper = LoggingHelper() +) -> torch.Tensor: + """The position of the object in the robot's root frame.""" + object: RigidObject = env.scene[object_cfg.name] + object_pos_w = object.data.root_pos_w[:, :3] + #print(f"target position : {object_pos_w}") + return object_pos_w \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/terminations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/terminations.py index 007a82d6ca83..6065810915a6 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/terminations.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/terminations.py @@ -16,6 +16,7 @@ from isaaclab.assets import RigidObject, Articulation from isaaclab.managers import SceneEntityCfg +from isaaclab.utils.math import combine_frame_transforms, matrix_from_quat from isaaclab.sensors import FrameTransformer from isaaclab.utils.math import combine_frame_transforms, subtract_frame_transforms @@ -94,4 +95,52 @@ def placed( #print("Gripper state : ",robot.data.joint_pos[:, -1] > gripper_open_val) gripper_open = robot.data.joint_pos[:, -1] > gripper_open_val state = torch.logical_and(object_position, gripper_open) - return state \ No newline at end of file + return state + +def object_stacked_upright(env: ManagerBasedRLEnv, robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + upper_object_cfg: SceneEntityCfg = SceneEntityCfg("object"),lower_object_cfg: SceneEntityCfg = SceneEntityCfg("goal"), + xy_threshold: float = 0.1, height_threshold: float = 0.1, height_diff: float = 0.05, + upright_good_deg: float = 30.0, gripper_open_val: torch.Tensor = torch.tensor([0.04]), logging=False) -> torch.Tensor: + """Stacked AND sufficiently upright (≤ upright_good_deg).""" + + upper: RigidObject = env.scene[upper_object_cfg.name] + tilt_deg = upright_tilt_deg(upper.data.root_quat_w, upper.data.default_root_state[:, 3:7]) + upright_good = tilt_deg <= upright_good_deg + stacked = object_stacked(env, robot_cfg, upper_object_cfg, lower_object_cfg, + xy_threshold, height_threshold, height_diff, gripper_open_val) + stacked_upright = stacked & upright_good + + #if logging: + #rising = log(env, "object_stacked_upright", stacked_upright, f"Stacked (upright ≤ {upright_good_deg}°)") + # idx = torch.nonzero(rising, as_tuple=False).squeeze(-1) + #if idx.numel() > 0: + # print(f"[stacked-upright tilt] n={int(idx.numel())} sample={tilt_deg[idx][:5].tolist()}") + + return stacked_upright + + +def upright_tilt_deg(q_cur, q_init) -> torch.Tensor: + u_cur, u_init = matrix_from_quat(q_cur)[:, :, 2], matrix_from_quat(q_init)[:, :, 2] + return torch.rad2deg(torch.acos((u_cur * u_init).sum(-1).clamp(-1.0, 1.0))) + + +def object_stacked(env: ManagerBasedRLEnv, robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + upper_object_cfg: SceneEntityCfg = SceneEntityCfg("object"), + lower_object_cfg: SceneEntityCfg = SceneEntityCfg("goal"), + xy_threshold: float = 0.1, height_threshold: float = 0.1, height_diff: float = 0.05, + gripper_open_val: torch.Tensor = torch.tensor([0.04]),logging=False) -> torch.Tensor: + """Check if an object is stacked by the specified robot.""" + + robot: Articulation = env.scene[robot_cfg.name] + upper_object: RigidObject = env.scene[upper_object_cfg.name] + lower_object: RigidObject = env.scene[lower_object_cfg.name] + pos_diff = upper_object.data.root_pos_w - lower_object.data.root_pos_w + height_dist = torch.linalg.vector_norm(pos_diff[:, 2:], dim=1) + xy_dist = torch.linalg.vector_norm(pos_diff[:, :2], dim=1) + stacked = torch.logical_and(xy_dist < xy_threshold, (height_dist - height_diff) < height_threshold) + # stacked = torch.logical_and(torch.isclose(robot.data.joint_pos[:, -1], + # gripper_open_val.to(env.device), atol=1e-4, rtol=1e-4), stacked) + # stacked = torch.logical_and(torch.isclose(robot.data.joint_pos[:, -2], + # gripper_open_val.to(env.device), atol=1e-4, rtol=1e-4), stacked) + print(f"For DEBUG : STACKED STATUS : {stacked}") + return stacked \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/__init__.py new file mode 100644 index 000000000000..236b2daab6e4 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configurations for the object stack environments.""" + +# We leave this file empty since we don't want to expose any configs in this package directly. +# We still need this file to import the "config" module in the parent package. diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/__init__.py new file mode 100644 index 000000000000..236b2daab6e4 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Configurations for the object stack environments.""" + +# We leave this file empty since we don't want to expose any configs in this package directly. +# We still need this file to import the "config" module in the parent package. diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/__init__.py new file mode 100644 index 000000000000..23bcce5f86b4 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/__init__.py @@ -0,0 +1,129 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +import gymnasium as gym +import os + +from . import ( + agents, + bin_stack_ik_rel_env_cfg, + stack_ik_abs_env_cfg, + stack_ik_rel_blueprint_env_cfg, + stack_ik_rel_env_cfg, + stack_ik_rel_env_cfg_skillgen, + stack_ik_rel_instance_randomize_env_cfg, + stack_ik_rel_visuomotor_cosmos_env_cfg, + stack_ik_rel_visuomotor_env_cfg, + stack_joint_pos_env_cfg, + stack_joint_pos_instance_randomize_env_cfg, +) + +## +# Register Gym environments. +## + +## +# Joint Position Control +## + +gym.register( + id="Isaac-Stack-Glassware-Franka-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": stack_joint_pos_env_cfg.FrankaCubeStackEnvCfg, + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Glassware-Instance-Randomize-Franka-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": stack_joint_pos_instance_randomize_env_cfg.FrankaCubeStackInstanceRandomizeEnvCfg, + }, + disable_env_checker=True, +) + + +## +# Inverse Kinematics - Relative Pose Control +## + +gym.register( + id="Isaac-Stack-Glassware-Franka-IK-Rel-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": stack_ik_rel_env_cfg.FrankaCubeStackEnvCfg, + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Glassware-Franka-IK-Rel-Visuomotor-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": stack_ik_rel_visuomotor_env_cfg.FrankaCubeStackVisuomotorEnvCfg, + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_image_84.json"), + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Glassware-Franka-IK-Rel-Visuomotor-Cosmos-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": stack_ik_rel_visuomotor_cosmos_env_cfg.FrankaCubeStackVisuomotorCosmosEnvCfg, + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_image_cosmos.json"), + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Glassware-Franka-IK-Abs-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": stack_ik_abs_env_cfg.FrankaCubeStackEnvCfg, + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Glassware-Instance-Randomize-Franka-IK-Rel-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": stack_ik_rel_instance_randomize_env_cfg.FrankaCubeStackInstanceRandomizeEnvCfg, + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Glassware-Franka-IK-Rel-Blueprint-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": stack_ik_rel_blueprint_env_cfg.FrankaCubeStackBlueprintEnvCfg, + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Glassware-Franka-IK-Rel-Skillgen-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": stack_ik_rel_env_cfg_skillgen.FrankaCubeStackSkillgenEnvCfg, + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Glassware-Bin-Franka-IK-Rel-Mimic-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": bin_stack_ik_rel_env_cfg.FrankaBinStackEnvCfg, + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), + }, + disable_env_checker=True, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/agents/__init__.py new file mode 100644 index 000000000000..2e924fbf1b13 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/agents/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/agents/robomimic/bc_rnn_image_84.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/agents/robomimic/bc_rnn_image_84.json new file mode 100644 index 000000000000..94e722fd0b11 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/agents/robomimic/bc_rnn_image_84.json @@ -0,0 +1,219 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_rnn_image_franka_stack", + "validate": false, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 20, + "epochs": [], + "on_best_validation": false, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 500, + "env": null, + "additional_envs": null, + "render": false, + "render_video": false, + "rollout": { + "enabled": false + } + }, + "train": { + "data": null, + "num_data_workers": 4, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": null, + "hdf5_validation_filter_key": null, + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 16, + "num_epochs": 600, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.1, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gaussian": { + "enabled": false, + "fixed_std": false, + "init_std": 0.1, + "min_std": 0.01, + "std_activation": "softplus", + "low_noise_eval": true + }, + "gmm": { + "enabled": true, + "num_modes": 5, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 1000, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos" + ], + "rgb": [ + "table_cam", + "wrist_cam" + ], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "flatten": true, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0, + "output_variance": false + } + }, + "obs_randomizer_class": "CropRandomizer", + "obs_randomizer_kwargs": { + "crop_height": 76, + "crop_width": 76, + "num_crops": 1, + "pos_enc": false + } + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + } +} diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/agents/robomimic/bc_rnn_image_cosmos.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/agents/robomimic/bc_rnn_image_cosmos.json new file mode 100644 index 000000000000..5f68551765b9 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/agents/robomimic/bc_rnn_image_cosmos.json @@ -0,0 +1,218 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_rnn_image_franka_stack_cosmos", + "validate": false, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 20, + "epochs": [], + "on_best_validation": false, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 500, + "env": null, + "additional_envs": null, + "render": false, + "render_video": false, + "rollout": { + "enabled": false + } + }, + "train": { + "data": null, + "num_data_workers": 4, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": null, + "hdf5_validation_filter_key": null, + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions", + "rewards", + "dones" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 16, + "num_epochs": 600, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.1, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gaussian": { + "enabled": false, + "fixed_std": false, + "init_std": 0.1, + "min_std": 0.01, + "std_activation": "softplus", + "low_noise_eval": true + }, + "gmm": { + "enabled": true, + "num_modes": 5, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false, + "latent_dim": 14, + "latent_clip": null, + "kl_weight": 1.0, + "decoder": { + "is_conditioned": true, + "reconstruction_sum_across_elements": false + }, + "prior": { + "learn": false, + "is_conditioned": false, + "use_gmm": false, + "gmm_num_modes": 10, + "gmm_learn_weights": false, + "use_categorical": false, + "categorical_dim": 10, + "categorical_gumbel_softmax_hard": false, + "categorical_init_temp": 1.0, + "categorical_temp_anneal_step": 0.001, + "categorical_min_temp": 0.3 + }, + "encoder_layer_dims": [ + 300, + 400 + ], + "decoder_layer_dims": [ + 300, + 400 + ], + "prior_layer_dims": [ + 300, + 400 + ] + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 1000, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + }, + "transformer": { + "enabled": false, + "context_length": 10, + "embed_dim": 512, + "num_layers": 6, + "num_heads": 8, + "emb_dropout": 0.1, + "attn_dropout": 0.1, + "block_output_dropout": 0.1, + "sinusoidal_embedding": false, + "activation": "gelu", + "supervise_all_steps": false, + "nn_parameter_for_timesteps": true + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos" + ], + "rgb": [ + "table_cam" + ], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "flatten": true, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0, + "output_variance": false + } + }, + "obs_randomizer_class": "CropRandomizer", + "obs_randomizer_kwargs": { + "crop_height": 180, + "crop_width": 180, + "num_crops": 1, + "pos_enc": false + } + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + } +} diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/agents/robomimic/bc_rnn_low_dim.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/agents/robomimic/bc_rnn_low_dim.json new file mode 100644 index 000000000000..23e490971f39 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/agents/robomimic/bc_rnn_low_dim.json @@ -0,0 +1,101 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_rnn_low_dim_franka_stack", + "validate": false, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 100, + "epochs": [], + "on_best_validation": false, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "env": null, + "additional_envs": null, + "render": false, + "render_video": false, + "rollout": { + "enabled": false + } + }, + "train": { + "data": null, + "num_data_workers": 4, + "hdf5_cache_mode": "all", + "hdf5_use_swmr": true, + "hdf5_normalize_obs": false, + "hdf5_filter_key": null, + "hdf5_validation_filter_key": null, + "seq_length": 10, + "dataset_keys": [ + "actions" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 101 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adam", + "learning_rate": { + "initial": 0.001, + "decay_factor": 0.1, + "epoch_schedule": [], + "scheduler_type": "multistep" + }, + "regularization": { + "L2": 0.0 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gmm": { + "enabled": true, + "num_modes": 5, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "rnn": { + "enabled": true, + "horizon": 10, + "hidden_dim": 400, + "rnn_type": "LSTM", + "num_layers": 2, + "open_loop": false, + "kwargs": { + "bidirectional": false + } + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "eef_pos", + "eef_quat", + "gripper_pos", + "object" + ], + "rgb": [], + "depth": [], + "scan": [] + } + } + } +} diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/bin_stack_ik_rel_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/bin_stack_ik_rel_env_cfg.py new file mode 100644 index 000000000000..fd4b386249e6 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/bin_stack_ik_rel_env_cfg.py @@ -0,0 +1,35 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass + +from . import bin_stack_joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +@configclass +class FrankaBinStackEnvCfg(bin_stack_joint_pos_env_cfg.FrankaBinStackEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/bin_stack_joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/bin_stack_joint_pos_env_cfg.py new file mode 100644 index 000000000000..c59153600af5 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/bin_stack_joint_pos_env_cfg.py @@ -0,0 +1,207 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +import isaaclab.sim as sim_utils +from isaaclab.assets import RigidObjectCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, ISAACLAB_NUCLEUS_DIR + +from isaaclab_tasks.manager_based.manipulation.stack_glassware import mdp +from isaaclab_tasks.manager_based.manipulation.stack_glassware.mdp import franka_stack_events +from isaaclab_tasks.manager_based.manipulation.stack_glassware.stack_env_cfg import StackEnvCfg + +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip +from isaaclab_assets.robots.franka import FRANKA_PANDA_CFG # isort: skip + + +@configclass +class EventCfg: + """Configuration for events.""" + + init_franka_arm_pose = EventTerm( + func=franka_stack_events.set_default_joint_pose, + # mode="startup", + mode="reset", + params={ + "default_pose": [0.0444, -0.1894, -0.1107, -2.5148, 0.0044, 2.3775, 0.6952, 0.0400, 0.0400], + }, + ) + + randomize_franka_joint_state = EventTerm( + func=franka_stack_events.randomize_joint_by_gaussian_offset, + mode="reset", + params={ + "mean": 0.0, + "std": 0.02, + "asset_cfg": SceneEntityCfg("robot"), + }, + ) + + # Reset blue bin position + reset_blue_bin_pose = EventTerm( + func=franka_stack_events.randomize_object_pose, + mode="reset", + params={ + # Keep bin at fixed position - no randomization + "pose_range": {"x": (0.4, 0.4), "y": (0.0, 0.0), "z": (0.0203, 0.0203), "yaw": (0.0, 0.0)}, + "min_separation": 0.0, + "asset_cfgs": [SceneEntityCfg("blue_sorting_bin")], + }, + ) + + # Reset cube 1 to initial position (inside the bin) + reset_cube_1_pose = EventTerm( + func=franka_stack_events.randomize_object_pose, + mode="reset", + params={ + "pose_range": {"x": (0.4, 0.4), "y": (0.0, 0.0), "z": (0.0203, 0.0203), "yaw": (0.0, 0.0)}, + "min_separation": 0.0, + "asset_cfgs": [SceneEntityCfg("cube_1")], + }, + ) + + # Reset cube 2 and 3 to initial position (outside the bin, to the left and right) + reset_cube_pose = EventTerm( + func=franka_stack_events.randomize_object_pose, + mode="reset", + params={ + "pose_range": {"x": (0.65, 0.70), "y": (-0.18, 0.18), "z": (0.0203, 0.0203), "yaw": (-1.0, 1.0, 0)}, + "min_separation": 0.1, + "asset_cfgs": [SceneEntityCfg("cube_2"), SceneEntityCfg("cube_3")], + }, + ) + + +@configclass +class FrankaBinStackEnvCfg(StackEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set events + self.events = EventCfg() + + # Set Franka as robot + self.scene.robot = FRANKA_PANDA_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.robot.spawn.semantic_tags = [("class", "robot")] + + # Add semantics to table + self.scene.table.spawn.semantic_tags = [("class", "table")] + + # Add semantics to ground + self.scene.plane.semantic_tags = [("class", "ground")] + + # Set actions for the specific robot type (franka) + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", joint_names=["panda_joint.*"], scale=0.5, use_default_offset=True + ) + self.actions.gripper_action = mdp.BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["panda_finger.*"], + open_command_expr={"panda_finger_.*": 0.04}, + close_command_expr={"panda_finger_.*": 0.0}, + ) + # utilities for gripper status check + self.gripper_joint_names = ["panda_finger_.*"] + self.gripper_open_val = 0.04 + self.gripper_threshold = 0.005 + + # Rigid body properties of each cube + cube_properties = RigidBodyPropertiesCfg( + solver_position_iteration_count=40, + solver_velocity_iteration_count=1, + max_angular_velocity=1000.0, + max_linear_velocity=1000.0, + max_depenetration_velocity=5.0, + disable_gravity=False, + ) + + # Blue sorting bin positioned at table center + self.scene.blue_sorting_bin = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/BlueSortingBin", + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.4, 0.0, 0.0203), rot=(1.0, 0.0, 0.0, 0.0)), + spawn=UsdFileCfg( + usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Mimic/nut_pour_task/nut_pour_assets/sorting_bin_blue.usd", + scale=(1.1, 1.6, 3.3), + rigid_props=sim_utils.RigidBodyPropertiesCfg(), + ), + ) + + # Cube 1 positioned at the bottom center of the blue bin + # The bin is at (0.4, 0.0, 0.0203), so cube_1 should be slightly above it + self.scene.cube_1 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_1", + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.4, 0.0, 0.025), rot=(1.0, 0.0, 0.0, 0.0)), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/blue_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ) + + # Cube 2 positioned outside the bin (to the right) + self.scene.cube_2 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_2", + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.85, 0.25, 0.0203), rot=(1.0, 0.0, 0.0, 0.0)), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/red_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ) + + # Cube 3 positioned outside the bin (to the left) + self.scene.cube_3 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_3", + init_state=RigidObjectCfg.InitialStateCfg(pos=(0.85, -0.25, 0.0203), rot=(1.0, 0.0, 0.0, 0.0)), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/green_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ) + + # Listens to the required transforms + marker_cfg = FRAME_MARKER_CFG.copy() + marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + marker_cfg.prim_path = "/Visuals/FrameTransformer" + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_link0", + debug_vis=False, + visualizer_cfg=marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_hand", + name="end_effector", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.0), + ), + ), + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_rightfinger", + name="tool_rightfinger", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.046), + ), + ), + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_leftfinger", + name="tool_leftfinger", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.046), + ), + ), + ], + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_abs_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_abs_env_cfg.py new file mode 100644 index 000000000000..17dbe0ce2a72 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_abs_env_cfg.py @@ -0,0 +1,59 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.openxr.openxr_device import OpenXRDevice, OpenXRDeviceCfg +from isaaclab.devices.openxr.retargeters.manipulator.gripper_retargeter import GripperRetargeterCfg +from isaaclab.devices.openxr.retargeters.manipulator.se3_abs_retargeter import Se3AbsRetargeterCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass + +from . import stack_joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +@configclass +class FrankaCubeStackEnvCfg(stack_joint_pos_env_cfg.FrankaCubeStackEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls"), + ) + + self.teleop_devices = DevicesCfg( + devices={ + "handtracking": OpenXRDeviceCfg( + retargeters=[ + Se3AbsRetargeterCfg( + bound_hand=OpenXRDevice.TrackingTarget.HAND_RIGHT, + zero_out_xy_rotation=True, + use_wrist_rotation=False, + use_wrist_position=True, + sim_device=self.sim.device, + ), + GripperRetargeterCfg( + bound_hand=OpenXRDevice.TrackingTarget.HAND_RIGHT, sim_device=self.sim.device + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + } + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_blueprint_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_blueprint_env_cfg.py new file mode 100644 index 000000000000..72ffa93a5ab4 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_blueprint_env_cfg.py @@ -0,0 +1,273 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import os +import torch +from torchvision.utils import save_image + +import isaaclab.sim as sim_utils +import isaaclab.utils.math as math_utils +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs import ManagerBasedEnv +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import Camera, CameraCfg, RayCasterCamera, TiledCamera +from isaaclab.utils import configclass + +from ... import mdp +from . import stack_joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +def image( + env: ManagerBasedEnv, + sensor_cfg: SceneEntityCfg = SceneEntityCfg("tiled_camera"), + data_type: str = "rgb", + convert_perspective_to_orthogonal: bool = False, + normalize: bool = True, + save_image_to_file: bool = False, + image_path: str = "image", +) -> torch.Tensor: + """Images of a specific datatype from the camera sensor. + + If the flag :attr:`normalize` is True, post-processing of the images are performed based on their + data-types: + + - "rgb": Scales the image to (0, 1) and subtracts with the mean of the current image batch. + - "depth" or "distance_to_camera" or "distance_to_plane": Replaces infinity values with zero. + + Args: + env: The environment the cameras are placed within. + sensor_cfg: The desired sensor to read from. Defaults to SceneEntityCfg("tiled_camera"). + data_type: The data type to pull from the desired camera. Defaults to "rgb". + convert_perspective_to_orthogonal: Whether to orthogonalize perspective depth images. + This is used only when the data type is "distance_to_camera". Defaults to False. + normalize: Whether to normalize the images. This depends on the selected data type. + Defaults to True. + + Returns: + The images produced at the last time-step + """ + # extract the used quantities (to enable type-hinting) + sensor: TiledCamera | Camera | RayCasterCamera = env.scene.sensors[sensor_cfg.name] + + # obtain the input image + images = sensor.data.output[data_type] + + # depth image conversion + if (data_type == "distance_to_camera") and convert_perspective_to_orthogonal: + images = math_utils.orthogonalize_perspective_depth(images, sensor.data.intrinsic_matrices) + + # rgb/depth image normalization + if normalize: + if data_type == "rgb": + images = images.float() / 255.0 + mean_tensor = torch.mean(images, dim=(1, 2), keepdim=True) + images -= mean_tensor + elif "distance_to" in data_type or "depth" in data_type: + images[images == float("inf")] = 0 + elif data_type == "normals": + images = (images + 1.0) * 0.5 + + if save_image_to_file: + dir_path, _ = os.path.split(image_path) + if dir_path: + os.makedirs(dir_path, exist_ok=True) + if images.dtype == torch.uint8: + images = images.float() / 255.0 + # Get total successful episodes + total_successes = 0 + if hasattr(env, "recorder_manager") and env.recorder_manager is not None: + total_successes = env.recorder_manager.exported_successful_episode_count + + for tile in range(images.shape[0]): + tile_chw = torch.swapaxes(images[tile : tile + 1].unsqueeze(1), 1, -1).squeeze(-1) + filename = ( + f"{image_path}_{data_type}_trial_{total_successes}_tile_{tile}_step_{env.common_step_counter}.png" + ) + save_image(tile_chw, filename) + + return images.clone() + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + object = ObsTerm(func=mdp.object_obs) + cube_positions = ObsTerm(func=mdp.cube_positions_in_world_frame) + cube_orientations = ObsTerm(func=mdp.cube_orientations_in_world_frame) + eef_pos = ObsTerm(func=mdp.ee_frame_pos) + eef_quat = ObsTerm(func=mdp.ee_frame_quat) + gripper_pos = ObsTerm(func=mdp.gripper_pos) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class RGBCameraPolicyCfg(ObsGroup): + """Observations for policy group with RGB images.""" + + table_cam_normals = ObsTerm( + func=image, + params={ + "sensor_cfg": SceneEntityCfg("table_cam"), + "data_type": "normals", + "normalize": True, + "save_image_to_file": True, + "image_path": "table_cam", + }, + ) + table_cam_segmentation = ObsTerm( + func=image, + params={ + "sensor_cfg": SceneEntityCfg("table_cam"), + "data_type": "semantic_segmentation", + "normalize": False, + "save_image_to_file": True, + "image_path": "table_cam", + }, + ) + table_high_cam_normals = ObsTerm( + func=image, + params={ + "sensor_cfg": SceneEntityCfg("table_high_cam"), + "data_type": "normals", + "normalize": True, + "save_image_to_file": True, + "image_path": "table_high_cam", + }, + ) + table_high_cam_segmentation = ObsTerm( + func=image, + params={ + "sensor_cfg": SceneEntityCfg("table_high_cam"), + "data_type": "semantic_segmentation", + "normalize": False, + "save_image_to_file": True, + "image_path": "table_high_cam", + }, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class SubtaskCfg(ObsGroup): + """Observations for subtask group.""" + + grasp_1 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_2"), + }, + ) + stack_1 = ObsTerm( + func=mdp.object_stacked, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "upper_object_cfg": SceneEntityCfg("cube_2"), + "lower_object_cfg": SceneEntityCfg("cube_1"), + }, + ) + grasp_2 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_3"), + }, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + rgb_camera: RGBCameraPolicyCfg = RGBCameraPolicyCfg() + subtask_terms: SubtaskCfg = SubtaskCfg() + + +@configclass +class FrankaCubeStackBlueprintEnvCfg(stack_joint_pos_env_cfg.FrankaCubeStackEnvCfg): + observations: ObservationsCfg = ObservationsCfg() + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.robot.spawn.semantic_tags = [("class", "robot")] + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + + MAPPING = { + "class:cube_1": (255, 36, 66, 255), + "class:cube_2": (255, 184, 48, 255), + "class:cube_3": (55, 255, 139, 255), + "class:table": (255, 237, 218, 255), + "class:ground": (100, 100, 100, 255), + "class:robot": (125, 125, 125, 255), + "class:UNLABELLED": (125, 125, 125, 255), + "class:BACKGROUND": (10, 10, 10, 255), + } + + # Set table view camera + self.scene.table_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/table_cam", + update_period=0.0333, + height=704, + width=1280, + data_types=["rgb", "semantic_segmentation", "normals"], + colorize_semantic_segmentation=True, + semantic_segmentation_mapping=MAPPING, + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5) + ), + offset=CameraCfg.OffsetCfg(pos=(1.0, 0.0, 0.33), rot=(-0.3799, 0.5963, 0.5963, -0.3799), convention="ros"), + ) + + # Set table view camera + self.scene.table_high_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/table_high_cam", + update_period=0.0333, + height=704, + width=1280, + data_types=["rgb", "semantic_segmentation", "normals"], + colorize_semantic_segmentation=True, + semantic_segmentation_mapping=MAPPING, + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(1.5, 1.0e5) + ), + offset=CameraCfg.OffsetCfg(pos=(1.4, 1.8, 1.2), rot=(-0.1393, 0.2025, 0.8185, -0.5192), convention="ros"), + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_env_cfg.py new file mode 100644 index 000000000000..f173ee644cef --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_env_cfg.py @@ -0,0 +1,69 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.keyboard import Se3KeyboardCfg +from isaaclab.devices.openxr.openxr_device import OpenXRDevice, OpenXRDeviceCfg +from isaaclab.devices.openxr.retargeters.manipulator.gripper_retargeter import GripperRetargeterCfg +from isaaclab.devices.openxr.retargeters.manipulator.se3_rel_retargeter import Se3RelRetargeterCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass + +from . import stack_joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +@configclass +class FrankaCubeStackEnvCfg(stack_joint_pos_env_cfg.FrankaCubeStackEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + + self.teleop_devices = DevicesCfg( + devices={ + "handtracking": OpenXRDeviceCfg( + retargeters=[ + Se3RelRetargeterCfg( + bound_hand=OpenXRDevice.TrackingTarget.HAND_RIGHT, + zero_out_xy_rotation=True, + use_wrist_rotation=False, + use_wrist_position=True, + delta_pos_scale_factor=10.0, + delta_rot_scale_factor=10.0, + sim_device=self.sim.device, + ), + GripperRetargeterCfg( + bound_hand=OpenXRDevice.TrackingTarget.HAND_RIGHT, sim_device=self.sim.device + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + "keyboard": Se3KeyboardCfg( + pos_sensitivity=0.05, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + } + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_env_cfg_skillgen.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_env_cfg_skillgen.py new file mode 100644 index 000000000000..b95640be8a7b --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_env_cfg_skillgen.py @@ -0,0 +1,167 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.keyboard import Se3KeyboardCfg +from isaaclab.devices.openxr.openxr_device import OpenXRDevice, OpenXRDeviceCfg +from isaaclab.devices.openxr.retargeters.manipulator.gripper_retargeter import GripperRetargeterCfg +from isaaclab.devices.openxr.retargeters.manipulator.se3_rel_retargeter import Se3RelRetargeterCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.utils import configclass + +from ... import mdp +from . import stack_joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group.""" + + actions = ObsTerm(func=mdp.last_action) + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + object = ObsTerm(func=mdp.object_obs) + cube_positions = ObsTerm(func=mdp.cube_positions_in_world_frame) + cube_orientations = ObsTerm(func=mdp.cube_orientations_in_world_frame) + eef_pos = ObsTerm(func=mdp.ee_frame_pos) + eef_quat = ObsTerm(func=mdp.ee_frame_quat) + gripper_pos = ObsTerm(func=mdp.gripper_pos) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class RGBCameraPolicyCfg(ObsGroup): + """Observations for policy group with RGB images.""" + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class SubtaskCfg(ObsGroup): + """Observations for subtask group.""" + + grasp_1 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_2"), + }, + ) + stack_1 = ObsTerm( + func=mdp.object_stacked, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "upper_object_cfg": SceneEntityCfg("cube_2"), + "lower_object_cfg": SceneEntityCfg("cube_1"), + }, + ) + grasp_2 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_3"), + }, + ) + stack_2 = ObsTerm( + func=mdp.object_stacked, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "upper_object_cfg": SceneEntityCfg("cube_3"), + "lower_object_cfg": SceneEntityCfg("cube_2"), + }, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + rgb_camera: RGBCameraPolicyCfg = RGBCameraPolicyCfg() + subtask_terms: SubtaskCfg = SubtaskCfg() + + +@configclass +class FrankaCubeStackSkillgenEnvCfg(stack_joint_pos_env_cfg.FrankaCubeStackEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Override observations with SkillGen-specific config + self.observations = ObservationsCfg() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + ) + + self.teleop_devices = DevicesCfg( + devices={ + "handtracking": OpenXRDeviceCfg( + retargeters=[ + Se3RelRetargeterCfg( + bound_hand=OpenXRDevice.TrackingTarget.HAND_RIGHT, + zero_out_xy_rotation=True, + use_wrist_rotation=False, + use_wrist_position=True, + delta_pos_scale_factor=10.0, + delta_rot_scale_factor=10.0, + sim_device=self.sim.device, + ), + GripperRetargeterCfg( + bound_hand=OpenXRDevice.TrackingTarget.HAND_RIGHT, sim_device=self.sim.device + ), + ], + sim_device=self.sim.device, + xr_cfg=self.xr, + ), + "keyboard": Se3KeyboardCfg( + pos_sensitivity=0.05, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + } + ) + + # Apply skillgen-specific cube position randomization + self.events.randomize_cube_positions.params["pose_range"] = { + "x": (0.45, 0.6), + "y": (-0.23, 0.23), + "z": (0.0203, 0.0203), + "yaw": (-1.0, 1, 0), + } + + # Set the offset for the end effector to be 0.0 + for f in self.scene.ee_frame.target_frames: + if f.name == "end_effector": + f.offset.pos = [0.0, 0.0, 0.0] + break diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_instance_randomize_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_instance_randomize_env_cfg.py new file mode 100644 index 000000000000..cee2530ee4f7 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_instance_randomize_env_cfg.py @@ -0,0 +1,41 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass + +from . import stack_joint_pos_instance_randomize_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +@configclass +class FrankaCubeStackInstanceRandomizeEnvCfg( + stack_joint_pos_instance_randomize_env_cfg.FrankaCubeStackInstanceRandomizeEnvCfg +): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Reduce the number of environments due to camera resources + self.scene.num_envs = 2 + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_visuomotor_cosmos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_visuomotor_cosmos_env_cfg.py new file mode 100644 index 000000000000..024dcd3795fa --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_visuomotor_cosmos_env_cfg.py @@ -0,0 +1,163 @@ +# Copyright (c) 2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import isaaclab.sim as sim_utils +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import CameraCfg +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.stack_glassware import mdp + +from . import stack_ik_rel_visuomotor_env_cfg + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + object = ObsTerm(func=mdp.object_obs) + cube_positions = ObsTerm(func=mdp.cube_positions_in_world_frame) + cube_orientations = ObsTerm(func=mdp.cube_orientations_in_world_frame) + eef_pos = ObsTerm(func=mdp.ee_frame_pos) + eef_quat = ObsTerm(func=mdp.ee_frame_quat) + gripper_pos = ObsTerm(func=mdp.gripper_pos) + table_cam = ObsTerm( + func=mdp.image, params={"sensor_cfg": SceneEntityCfg("table_cam"), "data_type": "rgb", "normalize": False} + ) + wrist_cam = ObsTerm( + func=mdp.image, params={"sensor_cfg": SceneEntityCfg("wrist_cam"), "data_type": "rgb", "normalize": False} + ) + table_cam_segmentation = ObsTerm( + func=mdp.image, + params={"sensor_cfg": SceneEntityCfg("table_cam"), "data_type": "semantic_segmentation", "normalize": True}, + ) + table_cam_normals = ObsTerm( + func=mdp.image, + params={"sensor_cfg": SceneEntityCfg("table_cam"), "data_type": "normals", "normalize": True}, + ) + table_cam_depth = ObsTerm( + func=mdp.image, + params={ + "sensor_cfg": SceneEntityCfg("table_cam"), + "data_type": "distance_to_image_plane", + "normalize": True, + }, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class SubtaskCfg(ObsGroup): + """Observations for subtask group.""" + + grasp_1 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_2"), + }, + ) + stack_1 = ObsTerm( + func=mdp.object_stacked, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "upper_object_cfg": SceneEntityCfg("cube_2"), + "lower_object_cfg": SceneEntityCfg("cube_1"), + }, + ) + grasp_2 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_3"), + }, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + subtask_terms: SubtaskCfg = SubtaskCfg() + + +@configclass +class FrankaCubeStackVisuomotorCosmosEnvCfg(stack_ik_rel_visuomotor_env_cfg.FrankaCubeStackVisuomotorEnvCfg): + observations: ObservationsCfg = ObservationsCfg() + + def __post_init__(self): + # post init of parent + super().__post_init__() + + import carb + from isaacsim.core.utils.carb import set_carb_setting + + carb_setting = carb.settings.get_settings() + set_carb_setting(carb_setting, "/rtx/domeLight/upperLowerStrategy", 4) + + SEMANTIC_MAPPING = { + "class:cube_1": (120, 230, 255, 255), + "class:cube_2": (255, 36, 66, 255), + "class:cube_3": (55, 255, 139, 255), + "class:table": (255, 237, 218, 255), + "class:ground": (100, 100, 100, 255), + "class:robot": (204, 110, 248, 255), + "class:UNLABELLED": (150, 150, 150, 255), + "class:BACKGROUND": (200, 200, 200, 255), + } + + # Set cameras + # Set wrist camera + self.scene.wrist_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_hand/wrist_cam", + update_period=0.0, + height=200, + width=200, + data_types=["rgb", "distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 2) + ), + offset=CameraCfg.OffsetCfg( + pos=(0.13, 0.0, -0.15), rot=(-0.70614, 0.03701, 0.03701, -0.70614), convention="ros" + ), + ) + + # Set table view camera + self.scene.table_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/table_cam", + update_period=0.0, + height=200, + width=200, + data_types=["rgb", "semantic_segmentation", "normals", "distance_to_image_plane"], + colorize_semantic_segmentation=True, + semantic_segmentation_mapping=SEMANTIC_MAPPING, + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 2) + ), + offset=CameraCfg.OffsetCfg( + pos=(1.0, 0.0, 0.4), rot=(0.35355, -0.61237, -0.61237, 0.35355), convention="ros" + ), + ) + + # Set settings for camera rendering + self.rerender_on_reset = True + self.sim.render.antialiasing_mode = "OFF" # disable dlss + + # List of image observations in policy observations + self.image_obs_list = ["table_cam", "wrist_cam"] diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_visuomotor_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_visuomotor_env_cfg.py new file mode 100644 index 000000000000..96604b2a1af0 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_visuomotor_env_cfg.py @@ -0,0 +1,238 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import isaaclab.sim as sim_utils +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import CameraCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, NVIDIA_NUCLEUS_DIR + +from isaaclab_tasks.manager_based.manipulation.stack_glassware import mdp +from isaaclab_tasks.manager_based.manipulation.stack_glassware.mdp import franka_stack_events + +from . import stack_joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip + + +@configclass +class EventCfg(stack_joint_pos_env_cfg.EventCfg): + """Configuration for events.""" + + randomize_light = EventTerm( + func=franka_stack_events.randomize_scene_lighting_domelight, + mode="reset", + params={ + "intensity_range": (1500.0, 10000.0), + "color_variation": 0.4, + "textures": [ + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Cloudy/abandoned_parking_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Cloudy/evening_road_01_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Cloudy/lakeside_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Indoor/autoshop_01_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Indoor/carpentry_shop_01_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Indoor/hospital_room_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Indoor/hotel_room_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Indoor/old_bus_depot_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Indoor/small_empty_house_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Indoor/surgery_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Studio/photo_studio_01_4k.hdr", + ], + "default_intensity": 3000.0, + "default_color": (0.75, 0.75, 0.75), + "default_texture": "", + }, + ) + + randomize_table_visual_material = EventTerm( + func=franka_stack_events.randomize_visual_texture_material, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("table"), + "textures": [ + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Ash/Ash_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Bamboo_Planks/Bamboo_Planks_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Birch/Birch_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Cherry/Cherry_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Mahogany_Planks/Mahogany_Planks_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Oak/Oak_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Plywood/Plywood_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Timber/Timber_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Timber_Cladding/Timber_Cladding_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Walnut_Planks/Walnut_Planks_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Stone/Marble/Marble_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Steel_Stainless/Steel_Stainless_BaseColor.png", + ], + "default_texture": ( + f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/Materials/Textures/DemoTable_TableBase_BaseColor.png" + ), + }, + ) + + randomize_robot_arm_visual_texture = EventTerm( + func=franka_stack_events.randomize_visual_texture_material, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("robot"), + "textures": [ + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Aluminum_Cast/Aluminum_Cast_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Aluminum_Polished/Aluminum_Polished_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Brass/Brass_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Bronze/Bronze_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Brushed_Antique_Copper/Brushed_Antique_Copper_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Cast_Metal_Silver_Vein/Cast_Metal_Silver_Vein_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Copper/Copper_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Gold/Gold_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Iron/Iron_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/RustedMetal/RustedMetal_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Silver/Silver_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Steel_Carbon/Steel_Carbon_BaseColor.png", + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Steel_Stainless/Steel_Stainless_BaseColor.png", + ], + }, + ) + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + object = ObsTerm(func=mdp.object_obs) + cube_positions = ObsTerm(func=mdp.cube_positions_in_world_frame) + cube_orientations = ObsTerm(func=mdp.cube_orientations_in_world_frame) + eef_pos = ObsTerm(func=mdp.ee_frame_pos) + eef_quat = ObsTerm(func=mdp.ee_frame_quat) + gripper_pos = ObsTerm(func=mdp.gripper_pos) + table_cam = ObsTerm( + func=mdp.image, params={"sensor_cfg": SceneEntityCfg("table_cam"), "data_type": "rgb", "normalize": False} + ) + wrist_cam = ObsTerm( + func=mdp.image, params={"sensor_cfg": SceneEntityCfg("wrist_cam"), "data_type": "rgb", "normalize": False} + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class SubtaskCfg(ObsGroup): + """Observations for subtask group.""" + + grasp_1 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_2"), + }, + ) + stack_1 = ObsTerm( + func=mdp.object_stacked, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "upper_object_cfg": SceneEntityCfg("cube_2"), + "lower_object_cfg": SceneEntityCfg("cube_1"), + }, + ) + grasp_2 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_3"), + }, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + subtask_terms: SubtaskCfg = SubtaskCfg() + + +@configclass +class FrankaCubeStackVisuomotorEnvCfg(stack_joint_pos_env_cfg.FrankaCubeStackEnvCfg): + observations: ObservationsCfg = ObservationsCfg() + + # Evaluation settings + eval_mode = False + eval_type = None + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set events + self.events = EventCfg() + + # Set Franka as robot + # We switch here to a stiffer PD controller for IK tracking to be better. + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.robot.spawn.semantic_tags = [("class", "robot")] + + # Set actions for the specific robot type (franka) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), + ) + + # Set cameras + # Set wrist camera + self.scene.wrist_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_hand/wrist_cam", + update_period=0.0, + height=84, + width=84, + data_types=["rgb", "distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 2) + ), + offset=CameraCfg.OffsetCfg( + pos=(0.13, 0.0, -0.15), rot=(-0.70614, 0.03701, 0.03701, -0.70614), convention="ros" + ), + ) + + # Set table view camera + self.scene.table_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/table_cam", + update_period=0.0, + height=84, + width=84, + data_types=["rgb", "distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 2) + ), + offset=CameraCfg.OffsetCfg( + pos=(1.0, 0.0, 0.4), rot=(0.35355, -0.61237, -0.61237, 0.35355), convention="ros" + ), + ) + + # Set settings for camera rendering + self.rerender_on_reset = True + self.sim.render.antialiasing_mode = "OFF" # disable dlss + + # List of image observations in policy observations + self.image_obs_list = ["table_cam", "wrist_cam"] diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_joint_pos_env_cfg.py new file mode 100644 index 000000000000..6b8561684fe0 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_joint_pos_env_cfg.py @@ -0,0 +1,168 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.assets import RigidObjectCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from isaaclab_tasks.manager_based.manipulation.stack_glassware import mdp +from isaaclab_tasks.manager_based.manipulation.stack_glassware.mdp import franka_stack_events +from isaaclab_tasks.manager_based.manipulation.stack_glassware.stack_env_cfg import StackEnvCfg + +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip +from isaaclab_assets.robots.franka import FRANKA_PANDA_CFG # isort: skip + + +@configclass +class EventCfg: + """Configuration for events.""" + + init_franka_arm_pose = EventTerm( + func=franka_stack_events.set_default_joint_pose, + mode="reset", + params={ + "default_pose": [0.0444, -0.1894, -0.1107, -2.5148, 0.0044, 2.3775, 0.6952, 0.0400, 0.0400], + }, + ) + + randomize_franka_joint_state = EventTerm( + func=franka_stack_events.randomize_joint_by_gaussian_offset, + mode="reset", + params={ + "mean": 0.0, + "std": 0.02, + "asset_cfg": SceneEntityCfg("robot"), + }, + ) + + randomize_cube_positions = EventTerm( + func=franka_stack_events.randomize_object_pose, + mode="reset", + params={ + "pose_range": {"x": (0.4, 0.6), "y": (-0.10, 0.10), "z": (0.0203, 0.0203), "yaw": (-1.0, 1, 0)}, + "min_separation": 0.1, + "asset_cfgs": [SceneEntityCfg("cube_1"), SceneEntityCfg("cube_2"), SceneEntityCfg("cube_3")], + }, + ) + + +@configclass +class FrankaCubeStackEnvCfg(StackEnvCfg): + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set events + self.events = EventCfg() + + # Set Franka as robot + self.scene.robot = FRANKA_PANDA_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.robot.spawn.semantic_tags = [("class", "robot")] + + # Add semantics to table + self.scene.table.spawn.semantic_tags = [("class", "table")] + + # Add semantics to ground + self.scene.plane.semantic_tags = [("class", "ground")] + + # Set actions for the specific robot type (franka) + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", joint_names=["panda_joint.*"], scale=0.5, use_default_offset=True + ) + self.actions.gripper_action = mdp.BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["panda_finger.*"], + open_command_expr={"panda_finger_.*": 0.04}, + close_command_expr={"panda_finger_.*": 0.0}, + ) + # utilities for gripper status check + self.gripper_joint_names = ["panda_finger_.*"] + self.gripper_open_val = 0.04 + self.gripper_threshold = 0.005 + + # Rigid body properties of each cube + cube_properties = RigidBodyPropertiesCfg( + solver_position_iteration_count=16, + solver_velocity_iteration_count=1, + max_angular_velocity=1000.0, + max_linear_velocity=1000.0, + max_depenetration_velocity=5.0, + disable_gravity=False, + ) + + # Set each stacking cube deterministically + self.scene.cube_1 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_1", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.4, 0.0, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/blue_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + semantic_tags=[("class", "cube_1")], + ), + ) + self.scene.cube_2 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_2", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.55, 0.05, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/red_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + semantic_tags=[("class", "cube_2")], + ), + ) + self.scene.cube_3 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_3", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.60, -0.1, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/green_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + semantic_tags=[("class", "cube_3")], + ), + ) + + # Listens to the required transforms + marker_cfg = FRAME_MARKER_CFG.copy() + marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + marker_cfg.prim_path = "/Visuals/FrameTransformer" + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_link0", + debug_vis=False, + visualizer_cfg=marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_hand", + name="end_effector", + offset=OffsetCfg( + pos=[0.0, 0.0, 0.1034], + ), + ), + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_rightfinger", + name="tool_rightfinger", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.046), + ), + ), + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_leftfinger", + name="tool_leftfinger", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.046), + ), + ), + ], + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_joint_pos_instance_randomize_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_joint_pos_instance_randomize_env_cfg.py new file mode 100644 index 000000000000..360f1f6482e1 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_joint_pos_instance_randomize_env_cfg.py @@ -0,0 +1,204 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import torch + +from isaaclab.assets import RigidObjectCfg, RigidObjectCollectionCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from isaaclab_tasks.manager_based.manipulation.stack_glassware import mdp +from isaaclab_tasks.manager_based.manipulation.stack_glassware.mdp import franka_stack_events +from isaaclab_tasks.manager_based.manipulation.stack_glassware.stack_instance_randomize_env_cfg import ( + StackInstanceRandomizeEnvCfg, +) + +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip +from isaaclab_assets.robots.franka import FRANKA_PANDA_CFG # isort: skip + + +@configclass +class EventCfg: + """Configuration for events.""" + + init_franka_arm_pose = EventTerm( + func=franka_stack_events.set_default_joint_pose, + mode="startup", + params={ + "default_pose": [0.0444, -0.1894, -0.1107, -2.5148, 0.0044, 2.3775, 0.6952, 0.0400, 0.0400], + }, + ) + + randomize_franka_joint_state = EventTerm( + func=franka_stack_events.randomize_joint_by_gaussian_offset, + mode="reset", + params={ + "mean": 0.0, + "std": 0.02, + "asset_cfg": SceneEntityCfg("robot"), + }, + ) + + randomize_cubes_in_focus = EventTerm( + func=franka_stack_events.randomize_rigid_objects_in_focus, + mode="reset", + params={ + "asset_cfgs": [SceneEntityCfg("cube_1"), SceneEntityCfg("cube_2"), SceneEntityCfg("cube_3")], + "out_focus_state": torch.tensor([10.0, 10.0, 10.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + "pose_range": {"x": (0.4, 0.6), "y": (-0.10, 0.10), "z": (0.0203, 0.0203), "yaw": (-1.0, 1, 0)}, + "min_separation": 0.1, + }, + ) + + +@configclass +class FrankaCubeStackInstanceRandomizeEnvCfg(StackInstanceRandomizeEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set events + self.events = EventCfg() + + # Set Franka as robot + self.scene.robot = FRANKA_PANDA_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Reduce the number of environments due to camera resources + self.scene.num_envs = 2 + + # Set actions for the specific robot type (franka) + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", joint_names=["panda_joint.*"], scale=0.5, use_default_offset=True + ) + self.actions.gripper_action = mdp.BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["panda_finger.*"], + open_command_expr={"panda_finger_.*": 0.04}, + close_command_expr={"panda_finger_.*": 0.0}, + ) + # utilities for gripper status check + self.gripper_joint_names = ["panda_finger_.*"] + self.gripper_open_val = 0.04 + self.gripper_threshold = 0.005 + + # Rigid body properties of each cube + cube_properties = RigidBodyPropertiesCfg( + solver_position_iteration_count=16, + solver_velocity_iteration_count=1, + max_angular_velocity=1000.0, + max_linear_velocity=1000.0, + max_depenetration_velocity=5.0, + disable_gravity=False, + ) + + # Set each stacking cube to be a collection of rigid objects + cube_1_config_dict = { + "blue_cube": RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_1_Blue", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.4, 0.0, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/blue_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ), + "red_cube": RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_1_Red", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.4, 0.0, 0.0403], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/red_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ), + } + + cube_2_config_dict = { + "red_cube": RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_2_Red", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.55, 0.05, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/red_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ), + "yellow_cube": RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_2_Yellow", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.55, 0.05, 0.0403], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/yellow_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ), + } + + cube_3_config_dict = { + "yellow_cube": RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_3_Yellow", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.60, -0.1, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/yellow_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ), + "green_cube": RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_2_Green", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.60, -0.1, 0.0403], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/green_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ), + } + + self.scene.cube_1 = RigidObjectCollectionCfg(rigid_objects=cube_1_config_dict) + self.scene.cube_2 = RigidObjectCollectionCfg(rigid_objects=cube_2_config_dict) + self.scene.cube_3 = RigidObjectCollectionCfg(rigid_objects=cube_3_config_dict) + + # Listens to the required transforms + marker_cfg = FRAME_MARKER_CFG.copy() + marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + marker_cfg.prim_path = "/Visuals/FrameTransformer" + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_link0", + debug_vis=False, + visualizer_cfg=marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_hand", + name="end_effector", + offset=OffsetCfg( + pos=[0.0, 0.0, 0.1034], + ), + ), + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_rightfinger", + name="tool_rightfinger", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.046), + ), + ), + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_leftfinger", + name="tool_leftfinger", + offset=OffsetCfg( + pos=(0.0, 0.0, 0.046), + ), + ), + ], + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/galbot/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/galbot/__init__.py new file mode 100644 index 000000000000..7aa2ebad0fce --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/galbot/__init__.py @@ -0,0 +1,73 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +import gymnasium as gym + +## +# Register Gym environments. +## + +## +# RMPFlow (with Joint Limit Constraint and Obstacle Avoidance) for Galbot Single Arm Cube Stack Task +# you can use for both absolute and relative mode, by given the USE_RELATIVE_MODE environment variable +## +gym.register( + id="Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-RmpFlow-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.stack_rmp_rel_env_cfg:RmpFlowGalbotLeftArmCubeStackEnvCfg", + }, + disable_env_checker=True, +) + + +gym.register( + id="Isaac-Stack-Cube-Galbot-Right-Arm-Suction-RmpFlow-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.stack_rmp_rel_env_cfg:RmpFlowGalbotRightArmCubeStackEnvCfg", + }, + disable_env_checker=True, +) + + +## +# Visuomotor Task for Galbot Left ArmCube Stack Task +## +gym.register( + id="Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.stack_rmp_rel_env_cfg:RmpFlowGalbotLeftArmCubeStackVisuomotorEnvCfg", + }, + disable_env_checker=True, +) + +## +# Policy Close-loop Evaluation Task for Galbot Left Arm Cube Stack Task (in Joint Space) +## +gym.register( + id="Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor-Joint-Position-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": ( + f"{__name__}.stack_rmp_rel_env_cfg:GalbotLeftArmJointPositionCubeStackVisuomotorEnvCfg_PLAY" + ), + }, + disable_env_checker=True, +) + +## +# Policy Close-loop Evaluation Task for Galbot Left Arm Cube Stack Task (in Task Space) +## +gym.register( + id="Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor-RmpFlow-Play-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.stack_rmp_rel_env_cfg:GalbotLeftArmRmpFlowCubeStackVisuomotorEnvCfg_PLAY", + }, + disable_env_checker=True, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/galbot/stack_joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/galbot/stack_joint_pos_env_cfg.py new file mode 100644 index 000000000000..66c4bc0c9a2e --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/galbot/stack_joint_pos_env_cfg.py @@ -0,0 +1,278 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +from isaaclab.assets import RigidObjectCfg, SurfaceGripperCfg +from isaaclab.envs.mdp.actions.actions_cfg import SurfaceGripperBinaryActionCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from isaaclab_tasks.manager_based.manipulation.stack_glassware import mdp +from isaaclab_tasks.manager_based.manipulation.stack_glassware.mdp import franka_stack_events +from isaaclab_tasks.manager_based.manipulation.stack_glassware.stack_env_cfg import ObservationsCfg, StackEnvCfg + +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip +from isaaclab_assets.robots.galbot import GALBOT_ONE_CHARLIE_CFG # isort: skip + + +@configclass +class EventCfg: + """Configuration for events.""" + + reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset", params={"reset_joint_targets": True}) + + randomize_cube_positions = EventTerm( + func=franka_stack_events.randomize_object_pose, + mode="reset", + params={ + "pose_range": { + "x": (-0.2, 0.0), + "y": (0.20, 0.40), + "z": (0.0203, 0.0203), + "yaw": (-1.0, 1.0, 0.0), + }, + "min_separation": 0.1, + "asset_cfgs": [SceneEntityCfg("cube_1"), SceneEntityCfg("cube_2"), SceneEntityCfg("cube_3")], + }, + ) + + +@configclass +class ObservationGalbotLeftArmGripperCfg: + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + + object = ObsTerm( + func=mdp.object_abs_obs_in_base_frame, + params={ + "robot_cfg": SceneEntityCfg("robot"), + }, + ) + cube_positions = ObsTerm( + func=mdp.cube_poses_in_base_frame, params={"robot_cfg": SceneEntityCfg("robot"), "return_key": "pos"} + ) + cube_orientations = ObsTerm( + func=mdp.cube_poses_in_base_frame, params={"robot_cfg": SceneEntityCfg("robot"), "return_key": "quat"} + ) + + eef_pos = ObsTerm( + func=mdp.ee_frame_pose_in_base_frame, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "return_key": "pos", + }, + ) + eef_quat = ObsTerm( + func=mdp.ee_frame_pose_in_base_frame, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "return_key": "quat", + }, + ) + gripper_pos = ObsTerm( + func=mdp.gripper_pos, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class SubtaskCfg(ObservationsCfg.SubtaskCfg): + """Observations for subtask group.""" + + grasp_1 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_2"), + }, + ) + stack_1 = ObsTerm( + func=mdp.object_stacked, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "upper_object_cfg": SceneEntityCfg("cube_2"), + "lower_object_cfg": SceneEntityCfg("cube_1"), + }, + ) + grasp_2 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_3"), + }, + ) + + def __post_init__(self): + super().__post_init__() + + @configclass + class RGBCameraPolicyCfg(ObsGroup): + """Observations for policy group with RGB images.""" + + table_cam = ObsTerm( + func=mdp.image, params={"sensor_cfg": SceneEntityCfg("table_cam"), "data_type": "rgb", "normalize": False} + ) + wrist_cam = ObsTerm( + func=mdp.image, params={"sensor_cfg": SceneEntityCfg("wrist_cam"), "data_type": "rgb", "normalize": False} + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + subtask_terms: SubtaskCfg = SubtaskCfg() + policy: PolicyCfg = PolicyCfg() + rgb_camera: RGBCameraPolicyCfg = RGBCameraPolicyCfg() + + +@configclass +class GalbotLeftArmCubeStackEnvCfg(StackEnvCfg): + + def __post_init__(self): + # post init of parent + super().__post_init__() + # MDP settings + + # Set events + self.events = EventCfg() + self.observations.policy = ObservationGalbotLeftArmGripperCfg().PolicyCfg() + self.observations.subtask_terms = ObservationGalbotLeftArmGripperCfg().SubtaskCfg() + + # Set galbot as robot + self.scene.robot = GALBOT_ONE_CHARLIE_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set actions for the specific robot type (galbot) + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", joint_names=["left_arm_joint.*"], scale=0.5, use_default_offset=True + ) + # Enable Parallel Gripper + self.actions.gripper_action = mdp.BinaryJointPositionActionCfg( + asset_name="robot", + joint_names=["left_gripper_.*_joint"], + open_command_expr={"left_gripper_.*_joint": 0.035}, + close_command_expr={"left_gripper_.*_joint": 0.0}, + ) + self.gripper_joint_names = ["left_gripper_.*_joint"] + self.gripper_open_val = 0.035 + self.gripper_threshold = 0.010 + + # Rigid body properties of each cube + cube_properties = RigidBodyPropertiesCfg( + solver_position_iteration_count=16, + solver_velocity_iteration_count=1, + max_angular_velocity=1000.0, + max_linear_velocity=1000.0, + max_depenetration_velocity=5.0, + disable_gravity=False, + ) + + # Set each stacking cube deterministically + self.scene.cube_1 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_1", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.4, 0.0, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/blue_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ) + self.scene.cube_2 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_2", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.55, 0.05, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/red_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ) + self.scene.cube_3 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_3", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.60, -0.1, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/green_block.usd", + scale=(1.0, 1.0, 1.0), + rigid_props=cube_properties, + ), + ) + + # Listens to the required transforms + self.marker_cfg = FRAME_MARKER_CFG.copy() + self.marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + self.marker_cfg.prim_path = "/Visuals/FrameTransformer" + + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/base_link", + debug_vis=False, + visualizer_cfg=self.marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/left_gripper_tcp_link", + name="end_effector", + offset=OffsetCfg( + pos=[0.0, 0.0, 0.0], + ), + ), + ], + ) + + +@configclass +class GalbotRightArmCubeStackEnvCfg(GalbotLeftArmCubeStackEnvCfg): + + def __post_init__(self): + # post init of parent + super().__post_init__() + + l, r = self.events.randomize_cube_positions.params["pose_range"]["y"] + self.events.randomize_cube_positions.params["pose_range"]["y"] = ( + -r, + -l, + ) # move to area below right hand + + # Set actions for the specific robot type (galbot) + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", joint_names=["right_arm_joint.*"], scale=0.5, use_default_offset=True + ) + + # Set surface gripper: Ensure the SurfaceGripper prim has the required attributes + self.scene.surface_gripper = SurfaceGripperCfg( + prim_path="{ENV_REGEX_NS}/Robot/right_suction_cup_tcp_link/SurfaceGripper", + max_grip_distance=0.02, + shear_force_limit=5000.0, + coaxial_force_limit=5000.0, + retry_interval=0.05, + ) + + # Set surface gripper action + self.actions.gripper_action = SurfaceGripperBinaryActionCfg( + asset_name="surface_gripper", + open_command=-1.0, + close_command=1.0, + ) + + self.scene.ee_frame.target_frames[0].prim_path = "{ENV_REGEX_NS}/Robot/right_suction_cup_tcp_link" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/galbot/stack_rmp_rel_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/galbot/stack_rmp_rel_env_cfg.py new file mode 100644 index 000000000000..f6e70e9038a2 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/galbot/stack_rmp_rel_env_cfg.py @@ -0,0 +1,282 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +import os + +import isaaclab.sim as sim_utils +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.keyboard import Se3KeyboardCfg +from isaaclab.devices.spacemouse import Se3SpaceMouseCfg +from isaaclab.envs.mdp.actions.rmpflow_actions_cfg import RMPFlowActionCfg +from isaaclab.sensors import CameraCfg, FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.stack_glassware import mdp + +from . import stack_joint_pos_env_cfg + +## +# Pre-defined configs +## +from isaaclab.controllers.config.rmp_flow import ( # isort: skip + GALBOT_LEFT_ARM_RMPFLOW_CFG, + GALBOT_RIGHT_ARM_RMPFLOW_CFG, +) +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip + + +## +# RmpFlow Controller for Galbot Left Arm Cube Stack Task (with Parallel Gripper) +## +@configclass +class RmpFlowGalbotLeftArmCubeStackEnvCfg(stack_joint_pos_env_cfg.GalbotLeftArmCubeStackEnvCfg): + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # read use_relative_mode from environment variable + # True for record_demos, and False for replay_demos, annotate_demos and generate_demos + use_relative_mode_env = os.getenv("USE_RELATIVE_MODE", "True") + self.use_relative_mode = use_relative_mode_env.lower() in ["true", "1", "t"] + + # Set actions for the specific robot type (Galbot) + self.actions.arm_action = RMPFlowActionCfg( + asset_name="robot", + joint_names=["left_arm_joint.*"], + body_name="left_gripper_tcp_link", + controller=GALBOT_LEFT_ARM_RMPFLOW_CFG, + scale=1.0, + body_offset=RMPFlowActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + articulation_prim_expr="/World/envs/env_.*/Robot", + use_relative_mode=self.use_relative_mode, + ) + + # Set the simulation parameters + self.sim.dt = 1 / 60 + self.sim.render_interval = 6 + + self.decimation = 3 + self.episode_length_s = 30.0 + + self.teleop_devices = DevicesCfg( + devices={ + "keyboard": Se3KeyboardCfg( + pos_sensitivity=0.05, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + "spacemouse": Se3SpaceMouseCfg( + pos_sensitivity=0.05, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + } + ) + + +## +# RmpFlow Controller for Galbot Right Arm Cube Stack Task (with Surface Gripper) +## +@configclass +class RmpFlowGalbotRightArmCubeStackEnvCfg(stack_joint_pos_env_cfg.GalbotRightArmCubeStackEnvCfg): + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # read use_relative_mode from environment variable + # True for record_demos, and False for replay_demos, annotate_demos and generate_demos + use_relative_mode_env = os.getenv("USE_RELATIVE_MODE", "True") + self.use_relative_mode = use_relative_mode_env.lower() in ["true", "1", "t"] + + # Set actions for the specific robot type (Galbot) + self.actions.arm_action = RMPFlowActionCfg( + asset_name="robot", + joint_names=["right_arm_joint.*"], + body_name="right_suction_cup_tcp_link", + controller=GALBOT_RIGHT_ARM_RMPFLOW_CFG, + scale=1.0, + body_offset=RMPFlowActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + articulation_prim_expr="/World/envs/env_.*/Robot", + use_relative_mode=self.use_relative_mode, + ) + # Set the simulation parameters + self.sim.dt = 1 / 60 + self.sim.render_interval = 1 + + self.decimation = 3 + self.episode_length_s = 30.0 + + self.teleop_devices = DevicesCfg( + devices={ + "keyboard": Se3KeyboardCfg( + pos_sensitivity=0.05, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + "spacemouse": Se3SpaceMouseCfg( + pos_sensitivity=0.05, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + } + ) + + +## +# Visuomotor Env for Record, Generate and Replay (in Task Space) +## +@configclass +class RmpFlowGalbotLeftArmCubeStackVisuomotorEnvCfg(RmpFlowGalbotLeftArmCubeStackEnvCfg): + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set left and right wrist cameras for VLA policy training + self.scene.right_wrist_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/Robot/right_arm_camera_sim_view_frame/right_camera", + update_period=0.0333, + height=256, + width=256, + data_types=["rgb", "distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=18.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5) + ), + offset=CameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(0.5, -0.5, 0.5, -0.5), convention="ros"), + ) + + self.scene.left_wrist_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/Robot/left_arm_camera_sim_view_frame/left_camera", + update_period=0.0333, + height=256, + width=256, + data_types=["rgb", "distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=18.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5) + ), + offset=CameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(0.5, -0.5, 0.5, -0.5), convention="ros"), + ) + + # Set ego view camera + self.scene.ego_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/Robot/head_camera_sim_view_frame/head_camera", + update_period=0.0333, + height=256, + width=256, + data_types=["rgb", "distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=18.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5) + ), + offset=CameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(0.5, -0.5, 0.5, -0.5), convention="ros"), + ) + + # Set front view camera + self.scene.front_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/front_camera", + update_period=0.0333, + height=256, + width=256, + data_types=["rgb", "distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5) + ), + offset=CameraCfg.OffsetCfg(pos=(1.0, 0.0, 0.6), rot=(-0.3799, 0.5963, 0.5963, -0.3799), convention="ros"), + ) + + marker_right_camera_cfg = FRAME_MARKER_CFG.copy() + marker_right_camera_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + marker_right_camera_cfg.prim_path = "/Visuals/FrameTransformerRightCamera" + + self.scene.right_arm_camera_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/base_link", + debug_vis=False, + visualizer_cfg=marker_right_camera_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/right_arm_camera_sim_view_frame", + name="right_camera", + offset=OffsetCfg( + pos=[0.0, 0.0, 0.0], + rot=(0.5, -0.5, 0.5, -0.5), + ), + ), + ], + ) + + marker_left_camera_cfg = FRAME_MARKER_CFG.copy() + marker_left_camera_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + marker_left_camera_cfg.prim_path = "/Visuals/FrameTransformerLeftCamera" + + self.scene.left_arm_camera_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/base_link", + debug_vis=False, + visualizer_cfg=marker_left_camera_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/left_arm_camera_sim_view_frame", + name="left_camera", + offset=OffsetCfg( + pos=[0.0, 0.0, 0.0], + rot=(0.5, -0.5, 0.5, -0.5), + ), + ), + ], + ) + + # Set settings for camera rendering + self.rerender_on_reset = True + self.sim.render.antialiasing_mode = "OFF" # disable dlss + + # List of image observations in policy observations + self.image_obs_list = ["ego_cam", "left_wrist_cam", "right_wrist_cam"] + + +## +# Task Env for VLA Policy Close-loop Evaluation (in Joint Space) +## + + +@configclass +class GalbotLeftArmJointPositionCubeStackVisuomotorEnvCfg_PLAY(RmpFlowGalbotLeftArmCubeStackVisuomotorEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", joint_names=["left_arm_joint.*"], scale=1.0, use_default_offset=False + ) + # Enable Parallel Gripper with AbsBinaryJointPosition Control + self.actions.gripper_action = mdp.AbsBinaryJointPositionActionCfg( + asset_name="robot", + threshold=0.030, + joint_names=["left_gripper_.*_joint"], + open_command_expr={"left_gripper_.*_joint": 0.035}, + close_command_expr={"left_gripper_.*_joint": 0.023}, + # real gripper close data is 0.0235, close to it to meet data distribution, but smaller to ensure robust grasping. + # during VLA inference, we set the close command to '0.023' since the VLA has never seen the gripper fully closed. + ) + + +## +# Task Envs for VLA Policy Close-loop Evaluation (in Task Space) +## +@configclass +class GalbotLeftArmRmpFlowCubeStackVisuomotorEnvCfg_PLAY(RmpFlowGalbotLeftArmCubeStackVisuomotorEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Enable Parallel Gripper with AbsBinaryJointPosition Control + self.actions.gripper_action = mdp.AbsBinaryJointPositionActionCfg( + asset_name="robot", + threshold=0.030, + joint_names=["left_gripper_.*_joint"], + open_command_expr={"left_gripper_.*_joint": 0.035}, + close_command_expr={"left_gripper_.*_joint": 0.023}, + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/ur10_gripper/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/ur10_gripper/__init__.py new file mode 100644 index 000000000000..41887a8df8b2 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/ur10_gripper/__init__.py @@ -0,0 +1,33 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +import gymnasium as gym + +## +# Register Gym environments. +## + + +## +# Inverse Kinematics - Relative Pose Control +## + +gym.register( + id="Isaac-Stack-Cube-UR10-Long-Suction-IK-Rel-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.stack_ik_rel_env_cfg:UR10LongSuctionCubeStackEnvCfg", + }, + disable_env_checker=True, +) + +gym.register( + id="Isaac-Stack-Cube-UR10-Short-Suction-IK-Rel-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.stack_ik_rel_env_cfg:UR10ShortSuctionCubeStackEnvCfg", + }, + disable_env_checker=True, +) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/ur10_gripper/stack_ik_rel_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/ur10_gripper/stack_ik_rel_env_cfg.py new file mode 100644 index 000000000000..00c379ef19fa --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/ur10_gripper/stack_ik_rel_env_cfg.py @@ -0,0 +1,80 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.devices.device_base import DevicesCfg +from isaaclab.devices.keyboard import Se3KeyboardCfg +from isaaclab.devices.spacemouse import Se3SpaceMouseCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass + +from . import stack_joint_pos_env_cfg + + +@configclass +class UR10LongSuctionCubeStackEnvCfg(stack_joint_pos_env_cfg.UR10LongSuctionCubeStackEnvCfg): + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set actions for the specific robot type (UR10 LONG SUCTION) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=[".*_joint"], + body_name="ee_link", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=1.0, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, -0.22]), + ) + + self.teleop_devices = DevicesCfg( + devices={ + "keyboard": Se3KeyboardCfg( + pos_sensitivity=0.02, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + "spacemouse": Se3SpaceMouseCfg( + pos_sensitivity=0.05, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + } + ) + + +@configclass +class UR10ShortSuctionCubeStackEnvCfg(stack_joint_pos_env_cfg.UR10ShortSuctionCubeStackEnvCfg): + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set actions for the specific robot type (UR10 SHORT SUCTION) + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=[".*_joint"], + body_name="ee_link", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=1.0, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, -0.159]), + ) + + self.teleop_devices = DevicesCfg( + devices={ + "keyboard": Se3KeyboardCfg( + pos_sensitivity=0.02, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + "spacemouse": Se3SpaceMouseCfg( + pos_sensitivity=0.05, + rot_sensitivity=0.05, + sim_device=self.sim.device, + ), + } + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/ur10_gripper/stack_joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/ur10_gripper/stack_joint_pos_env_cfg.py new file mode 100644 index 000000000000..7c27001b380f --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/ur10_gripper/stack_joint_pos_env_cfg.py @@ -0,0 +1,212 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.assets import RigidObjectCfg, SurfaceGripperCfg +from isaaclab.envs.mdp.actions.actions_cfg import SurfaceGripperBinaryActionCfg +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import FrameTransformerCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg +from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from isaaclab_tasks.manager_based.manipulation.stack_glassware import mdp +from isaaclab_tasks.manager_based.manipulation.stack_glassware.mdp import franka_stack_events +from isaaclab_tasks.manager_based.manipulation.stack_glassware.stack_env_cfg import StackEnvCfg + +from isaaclab_assets.robots.universal_robots import ( # isort: skip + UR10_LONG_SUCTION_CFG, + UR10_SHORT_SUCTION_CFG, +) + +## +# Pre-defined configs +## +from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip + + +@configclass +class EventCfgLongSuction: + """Configuration for events.""" + + init_franka_arm_pose = EventTerm( + func=franka_stack_events.set_default_joint_pose, + mode="reset", + params={ + "default_pose": [0.0, -1.5707, 1.5707, -1.5707, -1.5707, 0.0], + }, + ) + + randomize_franka_joint_state = EventTerm( + func=franka_stack_events.randomize_joint_by_gaussian_offset, + mode="reset", + params={ + "mean": 0.0, + "std": 0.02, + "asset_cfg": SceneEntityCfg("robot"), + }, + ) + + randomize_cube_positions = EventTerm( + func=franka_stack_events.randomize_object_pose, + mode="reset", + params={ + "pose_range": {"x": (0.4, 0.6), "y": (-0.10, 0.10), "z": (0.0203, 0.0203), "yaw": (-1.0, 1.0, 0)}, + "min_separation": 0.1, + "asset_cfgs": [SceneEntityCfg("cube_1"), SceneEntityCfg("cube_2"), SceneEntityCfg("cube_3")], + }, + ) + + +@configclass +class UR10CubeStackEnvCfg(StackEnvCfg): + # Rigid body properties of each cube + cube_properties = RigidBodyPropertiesCfg( + solver_position_iteration_count=16, + solver_velocity_iteration_count=1, + max_angular_velocity=1000.0, + max_linear_velocity=1000.0, + max_depenetration_velocity=5.0, + disable_gravity=False, + ) + cube_scale = (1.0, 1.0, 1.0) + # Listens to the required transforms + marker_cfg = FRAME_MARKER_CFG.copy() + marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) + marker_cfg.prim_path = "/Visuals/FrameTransformer" + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Set events + self.events = EventCfgLongSuction() + + # Set actions for the specific robot type (ur10) + self.actions.arm_action = mdp.JointPositionActionCfg( + asset_name="robot", joint_names=[".*_joint"], scale=0.5, use_default_offset=True + ) + # Set surface gripper action + self.actions.gripper_action = SurfaceGripperBinaryActionCfg( + asset_name="surface_gripper", + open_command=-1.0, + close_command=1.0, + ) + + # Set each stacking cube deterministically + self.scene.cube_1 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_1", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.4, 0.0, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/blue_block.usd", + scale=self.cube_scale, + rigid_props=self.cube_properties, + ), + ) + self.scene.cube_2 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_2", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.55, 0.05, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/red_block.usd", + scale=self.cube_scale, + rigid_props=self.cube_properties, + ), + ) + self.scene.cube_3 = RigidObjectCfg( + prim_path="{ENV_REGEX_NS}/Cube_3", + init_state=RigidObjectCfg.InitialStateCfg(pos=[0.60, -0.1, 0.0203], rot=[1, 0, 0, 0]), + spawn=UsdFileCfg( + usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/green_block.usd", + scale=self.cube_scale, + rigid_props=self.cube_properties, + ), + ) + + self.decimation = 5 + self.episode_length_s = 30.0 + # simulation settings + self.sim.dt = 0.01 # 100Hz + self.sim.render_interval = 5 + + +@configclass +class UR10LongSuctionCubeStackEnvCfg(UR10CubeStackEnvCfg): + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Suction grippers currently require CPU simulation + self.device = "cpu" + + # Set events + self.events = EventCfgLongSuction() + + # Set UR10 as robot + self.scene.robot = UR10_LONG_SUCTION_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set surface gripper: Ensure the SurfaceGripper prim has the required attributes + self.scene.surface_gripper = SurfaceGripperCfg( + prim_path="{ENV_REGEX_NS}/Robot/ee_link/SurfaceGripper", + max_grip_distance=0.05, + shear_force_limit=5000.0, + coaxial_force_limit=5000.0, + retry_interval=0.05, + ) + + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/base_link", + debug_vis=True, + visualizer_cfg=self.marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/ee_link", + name="end_effector", + offset=OffsetCfg( + pos=[0.22, 0.0, 0.0], + ), + ), + ], + ) + + +@configclass +class UR10ShortSuctionCubeStackEnvCfg(UR10CubeStackEnvCfg): + + def __post_init__(self): + # post init of parent + super().__post_init__() + + # Suction grippers currently require CPU simulation + self.device = "cpu" + + # Set UR10 as robot + self.scene.robot = UR10_SHORT_SUCTION_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + + # Set surface gripper: Ensure the SurfaceGripper prim has the required attributes + self.scene.surface_gripper = SurfaceGripperCfg( + prim_path="{ENV_REGEX_NS}/Robot/ee_link/SurfaceGripper", + max_grip_distance=0.05, + shear_force_limit=5000.0, + coaxial_force_limit=5000.0, + retry_interval=0.05, + ) + + self.scene.ee_frame = FrameTransformerCfg( + prim_path="{ENV_REGEX_NS}/Robot/base_link", + debug_vis=True, + visualizer_cfg=self.marker_cfg, + target_frames=[ + FrameTransformerCfg.FrameCfg( + prim_path="{ENV_REGEX_NS}/Robot/ee_link", + name="end_effector", + offset=OffsetCfg( + pos=[0.1585, 0.0, 0.0], + ), + ), + ], + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/__init__.py new file mode 100644 index 000000000000..8298b94d9b91 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""This sub-module contains the functions that are specific to the lift environments.""" + +from isaaclab.envs.mdp import * # noqa: F401, F403 + +from .observations import * # noqa: F401, F403 +from .terminations import * # noqa: F401, F403 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/franka_stack_events.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/franka_stack_events.py new file mode 100644 index 000000000000..009a44b1b372 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/franka_stack_events.py @@ -0,0 +1,314 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +from __future__ import annotations + +import math +import random +import torch +from typing import TYPE_CHECKING + +from isaacsim.core.utils.extensions import enable_extension + +import isaaclab.utils.math as math_utils +from isaaclab.assets import Articulation, AssetBase +from isaaclab.managers import SceneEntityCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + + +def set_default_joint_pose( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + default_pose: torch.Tensor, + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +): + # Set the default pose for robots in all envs + asset = env.scene[asset_cfg.name] + asset.data.default_joint_pos = torch.tensor(default_pose, device=env.device).repeat(env.num_envs, 1) + + +def randomize_joint_by_gaussian_offset( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + mean: float, + std: float, + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +): + asset: Articulation = env.scene[asset_cfg.name] + + # Add gaussian noise to joint states + joint_pos = asset.data.default_joint_pos[env_ids].clone() + joint_vel = asset.data.default_joint_vel[env_ids].clone() + joint_pos += math_utils.sample_gaussian(mean, std, joint_pos.shape, joint_pos.device) + + # Clamp joint pos to limits + joint_pos_limits = asset.data.soft_joint_pos_limits[env_ids] + joint_pos = joint_pos.clamp_(joint_pos_limits[..., 0], joint_pos_limits[..., 1]) + + # Don't noise the gripper poses + joint_pos[:, -2:] = asset.data.default_joint_pos[env_ids, -2:] + + # Set into the physics simulation + asset.set_joint_position_target(joint_pos, env_ids=env_ids) + asset.set_joint_velocity_target(joint_vel, env_ids=env_ids) + asset.write_joint_state_to_sim(joint_pos, joint_vel, env_ids=env_ids) + + +def sample_random_color(base=(0.75, 0.75, 0.75), variation=0.1): + """ + Generates a randomized color that stays close to the base color while preserving overall brightness. + The relative balance between the R, G, and B components is maintained by ensuring that + the sum of random offsets is zero. + + Parameters: + base (tuple): The base RGB color with each component between 0 and 1. + variation (float): Maximum deviation to sample for each channel before balancing. + + Returns: + tuple: A new RGB color with balanced random variation. + """ + # Generate random offsets for each channel in the range [-variation, variation] + offsets = [random.uniform(-variation, variation) for _ in range(3)] + # Compute the average offset + avg_offset = sum(offsets) / 3 + # Adjust offsets so their sum is zero (maintaining brightness) + balanced_offsets = [offset - avg_offset for offset in offsets] + + # Apply the balanced offsets to the base color and clamp each channel between 0 and 1 + new_color = tuple(max(0, min(1, base_component + offset)) for base_component, offset in zip(base, balanced_offsets)) + + return new_color + + +def randomize_scene_lighting_domelight( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + intensity_range: tuple[float, float], + color_variation: float, + textures: list[str], + default_intensity: float = 3000.0, + default_color: tuple[float, float, float] = (0.75, 0.75, 0.75), + default_texture: str = "", + asset_cfg: SceneEntityCfg = SceneEntityCfg("light"), +): + asset: AssetBase = env.scene[asset_cfg.name] + light_prim = asset.prims[0] + + intensity_attr = light_prim.GetAttribute("inputs:intensity") + intensity_attr.Set(default_intensity) + + color_attr = light_prim.GetAttribute("inputs:color") + color_attr.Set(default_color) + + texture_file_attr = light_prim.GetAttribute("inputs:texture:file") + texture_file_attr.Set(default_texture) + + if not hasattr(env.cfg, "eval_mode") or not env.cfg.eval_mode: + return + + if env.cfg.eval_type in ["light_intensity", "all"]: + # Sample new light intensity + new_intensity = random.uniform(intensity_range[0], intensity_range[1]) + # Set light intensity to light prim + intensity_attr.Set(new_intensity) + + if env.cfg.eval_type in ["light_color", "all"]: + # Sample new light color + new_color = sample_random_color(base=default_color, variation=color_variation) + # Set light color to light prim + color_attr.Set(new_color) + + if env.cfg.eval_type in ["light_texture", "all"]: + # Sample new light texture (background) + new_texture = random.sample(textures, 1)[0] + # Set light texture to light prim + texture_file_attr.Set(new_texture) + + +def sample_object_poses( + num_objects: int, + min_separation: float = 0.0, + pose_range: dict[str, tuple[float, float]] = {}, + max_sample_tries: int = 5000, +): + range_list = [pose_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z", "roll", "pitch", "yaw"]] + pose_list = [] + + for i in range(num_objects): + for j in range(max_sample_tries): + sample = [random.uniform(range[0], range[1]) for range in range_list] + + # Accept pose if it is the first one, or if reached max num tries + if len(pose_list) == 0 or j == max_sample_tries - 1: + pose_list.append(sample) + break + + # Check if pose of object is sufficiently far away from all other objects + separation_check = [math.dist(sample[:3], pose[:3]) > min_separation for pose in pose_list] + if False not in separation_check: + pose_list.append(sample) + break + + return pose_list + + +def randomize_object_pose( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + asset_cfgs: list[SceneEntityCfg], + min_separation: float = 0.0, + pose_range: dict[str, tuple[float, float]] = {}, + max_sample_tries: int = 5000, +): + if env_ids is None: + return + + # Randomize poses in each environment independently + for cur_env in env_ids.tolist(): + pose_list = sample_object_poses( + num_objects=len(asset_cfgs), + min_separation=min_separation, + pose_range=pose_range, + max_sample_tries=max_sample_tries, + ) + + # Randomize pose for each object + for i in range(len(asset_cfgs)): + asset_cfg = asset_cfgs[i] + asset = env.scene[asset_cfg.name] + + # Write pose to simulation + pose_tensor = torch.tensor([pose_list[i]], device=env.device) + positions = pose_tensor[:, 0:3] + env.scene.env_origins[cur_env, 0:3] + orientations = math_utils.quat_from_euler_xyz(pose_tensor[:, 3], pose_tensor[:, 4], pose_tensor[:, 5]) + asset.write_root_pose_to_sim( + torch.cat([positions, orientations], dim=-1), env_ids=torch.tensor([cur_env], device=env.device) + ) + asset.write_root_velocity_to_sim( + torch.zeros(1, 6, device=env.device), env_ids=torch.tensor([cur_env], device=env.device) + ) + + +def randomize_rigid_objects_in_focus( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + asset_cfgs: list[SceneEntityCfg], + out_focus_state: torch.Tensor, + min_separation: float = 0.0, + pose_range: dict[str, tuple[float, float]] = {}, + max_sample_tries: int = 5000, +): + if env_ids is None: + return + + # List of rigid objects in focus for each env (dim = [num_envs, num_rigid_objects]) + env.rigid_objects_in_focus = [] + + for cur_env in env_ids.tolist(): + # Sample in focus object poses + pose_list = sample_object_poses( + num_objects=len(asset_cfgs), + min_separation=min_separation, + pose_range=pose_range, + max_sample_tries=max_sample_tries, + ) + + selected_ids = [] + for asset_idx in range(len(asset_cfgs)): + asset_cfg = asset_cfgs[asset_idx] + asset = env.scene[asset_cfg.name] + + # Randomly select an object to bring into focus + object_id = random.randint(0, asset.num_objects - 1) + selected_ids.append(object_id) + + # Create object state tensor + object_states = torch.stack([out_focus_state] * asset.num_objects).to(device=env.device) + pose_tensor = torch.tensor([pose_list[asset_idx]], device=env.device) + positions = pose_tensor[:, 0:3] + env.scene.env_origins[cur_env, 0:3] + orientations = math_utils.quat_from_euler_xyz(pose_tensor[:, 3], pose_tensor[:, 4], pose_tensor[:, 5]) + object_states[object_id, 0:3] = positions + object_states[object_id, 3:7] = orientations + + asset.write_object_state_to_sim( + object_state=object_states, env_ids=torch.tensor([cur_env], device=env.device) + ) + + env.rigid_objects_in_focus.append(selected_ids) + + +def randomize_visual_texture_material( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + asset_cfg: SceneEntityCfg, + textures: list[str], + default_texture: str = "", + texture_rotation: tuple[float, float] = (0.0, 0.0), +): + """Randomize the visual texture of bodies on an asset using Replicator API. + + This function randomizes the visual texture of the bodies of the asset using the Replicator API. + The function samples random textures from the given texture paths and applies them to the bodies + of the asset. The textures are projected onto the bodies and rotated by the given angles. + + .. note:: + The function assumes that the asset follows the prim naming convention as: + "{asset_prim_path}/{body_name}/visuals" where the body name is the name of the body to + which the texture is applied. This is the default prim ordering when importing assets + from the asset converters in Isaac Lab. + + .. note:: + When randomizing the texture of individual assets, please make sure to set + :attr:`isaaclab.scene.InteractiveSceneCfg.replicate_physics` to False. This ensures that physics + parser will parse the individual asset properties separately. + """ + if hasattr(env.cfg, "eval_mode") and ( + not env.cfg.eval_mode or env.cfg.eval_type not in [f"{asset_cfg.name}_texture", "all"] + ): + return + # textures = [default_texture] + + # enable replicator extension if not already enabled + enable_extension("omni.replicator.core") + # we import the module here since we may not always need the replicator + import omni.replicator.core as rep + + # check to make sure replicate_physics is set to False, else raise error + # note: We add an explicit check here since texture randomization can happen outside of 'prestartup' mode + # and the event manager doesn't check in that case. + if env.cfg.scene.replicate_physics: + raise RuntimeError( + "Unable to randomize visual texture material with scene replication enabled." + " For stable USD-level randomization, please disable scene replication" + " by setting 'replicate_physics' to False in 'InteractiveSceneCfg'." + ) + + # convert from radians to degrees + texture_rotation = tuple(math.degrees(angle) for angle in texture_rotation) + + # obtain the asset entity + asset = env.scene[asset_cfg.name] + + # join all bodies in the asset + body_names = asset_cfg.body_names + if isinstance(body_names, str): + body_names_regex = body_names + elif isinstance(body_names, list): + body_names_regex = "|".join(body_names) + else: + body_names_regex = ".*" + + if not hasattr(asset, "cfg"): + prims_group = rep.get.prims(path_pattern=f"{asset.prim_paths[0]}/visuals") + else: + prims_group = rep.get.prims(path_pattern=f"{asset.cfg.prim_path}/{body_names_regex}/visuals") + + with prims_group: + rep.randomizer.texture( + textures=textures, project_uvw=True, texture_rotate=rep.distribution.uniform(*texture_rotation) + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/observations.py new file mode 100644 index 000000000000..2f65cd916ee9 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/observations.py @@ -0,0 +1,532 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import torch +from typing import TYPE_CHECKING, Literal + +import isaaclab.utils.math as math_utils +from isaaclab.assets import Articulation, RigidObject, RigidObjectCollection +from isaaclab.managers import SceneEntityCfg +from isaaclab.sensors import FrameTransformer + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def cube_positions_in_world_frame( + env: ManagerBasedRLEnv, + cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), + cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), + cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), +) -> torch.Tensor: + """The position of the cubes in the world frame.""" + cube_1: RigidObject = env.scene[cube_1_cfg.name] + cube_2: RigidObject = env.scene[cube_2_cfg.name] + cube_3: RigidObject = env.scene[cube_3_cfg.name] + + return torch.cat((cube_1.data.root_pos_w, cube_2.data.root_pos_w, cube_3.data.root_pos_w), dim=1) + + +def instance_randomize_cube_positions_in_world_frame( + env: ManagerBasedRLEnv, + cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), + cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), + cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), +) -> torch.Tensor: + """The position of the cubes in the world frame.""" + if not hasattr(env, "rigid_objects_in_focus"): + return torch.full((env.num_envs, 9), fill_value=-1) + + cube_1: RigidObjectCollection = env.scene[cube_1_cfg.name] + cube_2: RigidObjectCollection = env.scene[cube_2_cfg.name] + cube_3: RigidObjectCollection = env.scene[cube_3_cfg.name] + + cube_1_pos_w = [] + cube_2_pos_w = [] + cube_3_pos_w = [] + for env_id in range(env.num_envs): + cube_1_pos_w.append(cube_1.data.object_pos_w[env_id, env.rigid_objects_in_focus[env_id][0], :3]) + cube_2_pos_w.append(cube_2.data.object_pos_w[env_id, env.rigid_objects_in_focus[env_id][1], :3]) + cube_3_pos_w.append(cube_3.data.object_pos_w[env_id, env.rigid_objects_in_focus[env_id][2], :3]) + cube_1_pos_w = torch.stack(cube_1_pos_w) + cube_2_pos_w = torch.stack(cube_2_pos_w) + cube_3_pos_w = torch.stack(cube_3_pos_w) + + return torch.cat((cube_1_pos_w, cube_2_pos_w, cube_3_pos_w), dim=1) + + +def cube_orientations_in_world_frame( + env: ManagerBasedRLEnv, + cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), + cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), + cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), +): + """The orientation of the cubes in the world frame.""" + cube_1: RigidObject = env.scene[cube_1_cfg.name] + cube_2: RigidObject = env.scene[cube_2_cfg.name] + cube_3: RigidObject = env.scene[cube_3_cfg.name] + + return torch.cat((cube_1.data.root_quat_w, cube_2.data.root_quat_w, cube_3.data.root_quat_w), dim=1) + + +def instance_randomize_cube_orientations_in_world_frame( + env: ManagerBasedRLEnv, + cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), + cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), + cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), +) -> torch.Tensor: + """The orientation of the cubes in the world frame.""" + if not hasattr(env, "rigid_objects_in_focus"): + return torch.full((env.num_envs, 9), fill_value=-1) + + cube_1: RigidObjectCollection = env.scene[cube_1_cfg.name] + cube_2: RigidObjectCollection = env.scene[cube_2_cfg.name] + cube_3: RigidObjectCollection = env.scene[cube_3_cfg.name] + + cube_1_quat_w = [] + cube_2_quat_w = [] + cube_3_quat_w = [] + for env_id in range(env.num_envs): + cube_1_quat_w.append(cube_1.data.object_quat_w[env_id, env.rigid_objects_in_focus[env_id][0], :4]) + cube_2_quat_w.append(cube_2.data.object_quat_w[env_id, env.rigid_objects_in_focus[env_id][1], :4]) + cube_3_quat_w.append(cube_3.data.object_quat_w[env_id, env.rigid_objects_in_focus[env_id][2], :4]) + cube_1_quat_w = torch.stack(cube_1_quat_w) + cube_2_quat_w = torch.stack(cube_2_quat_w) + cube_3_quat_w = torch.stack(cube_3_quat_w) + + return torch.cat((cube_1_quat_w, cube_2_quat_w, cube_3_quat_w), dim=1) + + +def object_obs( + env: ManagerBasedRLEnv, + cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), + cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), + cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), + ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), +): + """ + Object observations (in world frame): + cube_1 pos, + cube_1 quat, + cube_2 pos, + cube_2 quat, + cube_3 pos, + cube_3 quat, + gripper to cube_1, + gripper to cube_2, + gripper to cube_3, + cube_1 to cube_2, + cube_2 to cube_3, + cube_1 to cube_3, + """ + cube_1: RigidObject = env.scene[cube_1_cfg.name] + cube_2: RigidObject = env.scene[cube_2_cfg.name] + cube_3: RigidObject = env.scene[cube_3_cfg.name] + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + + cube_1_pos_w = cube_1.data.root_pos_w + cube_1_quat_w = cube_1.data.root_quat_w + + cube_2_pos_w = cube_2.data.root_pos_w + cube_2_quat_w = cube_2.data.root_quat_w + + cube_3_pos_w = cube_3.data.root_pos_w + cube_3_quat_w = cube_3.data.root_quat_w + + ee_pos_w = ee_frame.data.target_pos_w[:, 0, :] + gripper_to_cube_1 = cube_1_pos_w - ee_pos_w + gripper_to_cube_2 = cube_2_pos_w - ee_pos_w + gripper_to_cube_3 = cube_3_pos_w - ee_pos_w + + cube_1_to_2 = cube_1_pos_w - cube_2_pos_w + cube_2_to_3 = cube_2_pos_w - cube_3_pos_w + cube_1_to_3 = cube_1_pos_w - cube_3_pos_w + + return torch.cat( + ( + cube_1_pos_w - env.scene.env_origins, + cube_1_quat_w, + cube_2_pos_w - env.scene.env_origins, + cube_2_quat_w, + cube_3_pos_w - env.scene.env_origins, + cube_3_quat_w, + gripper_to_cube_1, + gripper_to_cube_2, + gripper_to_cube_3, + cube_1_to_2, + cube_2_to_3, + cube_1_to_3, + ), + dim=1, + ) + + +def instance_randomize_object_obs( + env: ManagerBasedRLEnv, + cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), + cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), + cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), + ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), +): + """ + Object observations (in world frame): + cube_1 pos, + cube_1 quat, + cube_2 pos, + cube_2 quat, + cube_3 pos, + cube_3 quat, + gripper to cube_1, + gripper to cube_2, + gripper to cube_3, + cube_1 to cube_2, + cube_2 to cube_3, + cube_1 to cube_3, + """ + if not hasattr(env, "rigid_objects_in_focus"): + return torch.full((env.num_envs, 9), fill_value=-1) + + cube_1: RigidObjectCollection = env.scene[cube_1_cfg.name] + cube_2: RigidObjectCollection = env.scene[cube_2_cfg.name] + cube_3: RigidObjectCollection = env.scene[cube_3_cfg.name] + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + + cube_1_pos_w = [] + cube_2_pos_w = [] + cube_3_pos_w = [] + cube_1_quat_w = [] + cube_2_quat_w = [] + cube_3_quat_w = [] + for env_id in range(env.num_envs): + cube_1_pos_w.append(cube_1.data.object_pos_w[env_id, env.rigid_objects_in_focus[env_id][0], :3]) + cube_2_pos_w.append(cube_2.data.object_pos_w[env_id, env.rigid_objects_in_focus[env_id][1], :3]) + cube_3_pos_w.append(cube_3.data.object_pos_w[env_id, env.rigid_objects_in_focus[env_id][2], :3]) + cube_1_quat_w.append(cube_1.data.object_quat_w[env_id, env.rigid_objects_in_focus[env_id][0], :4]) + cube_2_quat_w.append(cube_2.data.object_quat_w[env_id, env.rigid_objects_in_focus[env_id][1], :4]) + cube_3_quat_w.append(cube_3.data.object_quat_w[env_id, env.rigid_objects_in_focus[env_id][2], :4]) + cube_1_pos_w = torch.stack(cube_1_pos_w) + cube_2_pos_w = torch.stack(cube_2_pos_w) + cube_3_pos_w = torch.stack(cube_3_pos_w) + cube_1_quat_w = torch.stack(cube_1_quat_w) + cube_2_quat_w = torch.stack(cube_2_quat_w) + cube_3_quat_w = torch.stack(cube_3_quat_w) + + ee_pos_w = ee_frame.data.target_pos_w[:, 0, :] + gripper_to_cube_1 = cube_1_pos_w - ee_pos_w + gripper_to_cube_2 = cube_2_pos_w - ee_pos_w + gripper_to_cube_3 = cube_3_pos_w - ee_pos_w + + cube_1_to_2 = cube_1_pos_w - cube_2_pos_w + cube_2_to_3 = cube_2_pos_w - cube_3_pos_w + cube_1_to_3 = cube_1_pos_w - cube_3_pos_w + + return torch.cat( + ( + cube_1_pos_w - env.scene.env_origins, + cube_1_quat_w, + cube_2_pos_w - env.scene.env_origins, + cube_2_quat_w, + cube_3_pos_w - env.scene.env_origins, + cube_3_quat_w, + gripper_to_cube_1, + gripper_to_cube_2, + gripper_to_cube_3, + cube_1_to_2, + cube_2_to_3, + cube_1_to_3, + ), + dim=1, + ) + + +def ee_frame_pos(env: ManagerBasedRLEnv, ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame")) -> torch.Tensor: + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + ee_frame_pos = ee_frame.data.target_pos_w[:, 0, :] - env.scene.env_origins[:, 0:3] + + return ee_frame_pos + + +def ee_frame_quat(env: ManagerBasedRLEnv, ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame")) -> torch.Tensor: + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + ee_frame_quat = ee_frame.data.target_quat_w[:, 0, :] + + return ee_frame_quat + + +def gripper_pos( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +) -> torch.Tensor: + """ + Obtain the versatile gripper position of both Gripper and Suction Cup. + """ + robot: Articulation = env.scene[robot_cfg.name] + + if hasattr(env.scene, "surface_grippers") and len(env.scene.surface_grippers) > 0: + # Handle multiple surface grippers by concatenating their states + gripper_states = [] + for gripper_name, surface_gripper in env.scene.surface_grippers.items(): + gripper_states.append(surface_gripper.state.view(-1, 1)) + + if len(gripper_states) == 1: + return gripper_states[0] + else: + return torch.cat(gripper_states, dim=1) + + else: + if hasattr(env.cfg, "gripper_joint_names"): + gripper_joint_ids, _ = robot.find_joints(env.cfg.gripper_joint_names) + assert len(gripper_joint_ids) == 2, "Observation gripper_pos only support parallel gripper for now" + finger_joint_1 = robot.data.joint_pos[:, gripper_joint_ids[0]].clone().unsqueeze(1) + finger_joint_2 = -1 * robot.data.joint_pos[:, gripper_joint_ids[1]].clone().unsqueeze(1) + return torch.cat((finger_joint_1, finger_joint_2), dim=1) + else: + raise NotImplementedError("[Error] Cannot find gripper_joint_names in the environment config") + + +def object_grasped( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg, + ee_frame_cfg: SceneEntityCfg, + object_cfg: SceneEntityCfg, + diff_threshold: float = 0.06, +) -> torch.Tensor: + """Check if an object is grasped by the specified robot.""" + + robot: Articulation = env.scene[robot_cfg.name] + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + object: RigidObject = env.scene[object_cfg.name] + + object_pos = object.data.root_pos_w + end_effector_pos = ee_frame.data.target_pos_w[:, 0, :] + pose_diff = torch.linalg.vector_norm(object_pos - end_effector_pos, dim=1) + + if hasattr(env.scene, "surface_grippers") and len(env.scene.surface_grippers) > 0: + surface_gripper = env.scene.surface_grippers["surface_gripper"] + suction_cup_status = surface_gripper.state.view(-1, 1) # 1: closed, 0: closing, -1: open + suction_cup_is_closed = (suction_cup_status == 1).to(torch.float32) + grasped = torch.logical_and(suction_cup_is_closed, pose_diff < diff_threshold) + + else: + if hasattr(env.cfg, "gripper_joint_names"): + gripper_joint_ids, _ = robot.find_joints(env.cfg.gripper_joint_names) + assert len(gripper_joint_ids) == 2, "Observations only support parallel gripper for now" + + grasped = torch.logical_and( + pose_diff < diff_threshold, + torch.abs( + robot.data.joint_pos[:, gripper_joint_ids[0]] + - torch.tensor(env.cfg.gripper_open_val, dtype=torch.float32).to(env.device) + ) + > env.cfg.gripper_threshold, + ) + grasped = torch.logical_and( + grasped, + torch.abs( + robot.data.joint_pos[:, gripper_joint_ids[1]] + - torch.tensor(env.cfg.gripper_open_val, dtype=torch.float32).to(env.device) + ) + > env.cfg.gripper_threshold, + ) + + return grasped + + +def object_stacked( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg, + upper_object_cfg: SceneEntityCfg, + lower_object_cfg: SceneEntityCfg, + xy_threshold: float = 0.05, + height_threshold: float = 0.005, + height_diff: float = 0.0468, +) -> torch.Tensor: + """Check if an object is stacked by the specified robot.""" + + robot: Articulation = env.scene[robot_cfg.name] + upper_object: RigidObject = env.scene[upper_object_cfg.name] + lower_object: RigidObject = env.scene[lower_object_cfg.name] + + pos_diff = upper_object.data.root_pos_w - lower_object.data.root_pos_w + height_dist = torch.linalg.vector_norm(pos_diff[:, 2:], dim=1) + xy_dist = torch.linalg.vector_norm(pos_diff[:, :2], dim=1) + + stacked = torch.logical_and(xy_dist < xy_threshold, (height_dist - height_diff) < height_threshold) + + if hasattr(env.scene, "surface_grippers") and len(env.scene.surface_grippers) > 0: + surface_gripper = env.scene.surface_grippers["surface_gripper"] + suction_cup_status = surface_gripper.state.view(-1, 1) # 1: closed, 0: closing, -1: open + suction_cup_is_open = (suction_cup_status == -1).to(torch.float32) + stacked = torch.logical_and(suction_cup_is_open, stacked) + + else: + if hasattr(env.cfg, "gripper_joint_names"): + gripper_joint_ids, _ = robot.find_joints(env.cfg.gripper_joint_names) + assert len(gripper_joint_ids) == 2, "Observations only support parallel gripper for now" + stacked = torch.logical_and( + torch.isclose( + robot.data.joint_pos[:, gripper_joint_ids[0]], + torch.tensor(env.cfg.gripper_open_val, dtype=torch.float32).to(env.device), + atol=1e-4, + rtol=1e-4, + ), + stacked, + ) + stacked = torch.logical_and( + torch.isclose( + robot.data.joint_pos[:, gripper_joint_ids[1]], + torch.tensor(env.cfg.gripper_open_val, dtype=torch.float32).to(env.device), + atol=1e-4, + rtol=1e-4, + ), + stacked, + ) + else: + raise ValueError("No gripper_joint_names found in environment config") + + return stacked + + +def cube_poses_in_base_frame( + env: ManagerBasedRLEnv, + cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), + cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), + cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + return_key: Literal["pos", "quat", None] = None, +) -> torch.Tensor: + """The position and orientation of the cubes in the robot base frame.""" + + cube_1: RigidObject = env.scene[cube_1_cfg.name] + cube_2: RigidObject = env.scene[cube_2_cfg.name] + cube_3: RigidObject = env.scene[cube_3_cfg.name] + + pos_cube_1_world = cube_1.data.root_pos_w + pos_cube_2_world = cube_2.data.root_pos_w + pos_cube_3_world = cube_3.data.root_pos_w + + quat_cube_1_world = cube_1.data.root_quat_w + quat_cube_2_world = cube_2.data.root_quat_w + quat_cube_3_world = cube_3.data.root_quat_w + + robot: Articulation = env.scene[robot_cfg.name] + root_pos_w = robot.data.root_pos_w + root_quat_w = robot.data.root_quat_w + + pos_cube_1_base, quat_cube_1_base = math_utils.subtract_frame_transforms( + root_pos_w, root_quat_w, pos_cube_1_world, quat_cube_1_world + ) + pos_cube_2_base, quat_cube_2_base = math_utils.subtract_frame_transforms( + root_pos_w, root_quat_w, pos_cube_2_world, quat_cube_2_world + ) + pos_cube_3_base, quat_cube_3_base = math_utils.subtract_frame_transforms( + root_pos_w, root_quat_w, pos_cube_3_world, quat_cube_3_world + ) + + pos_cubes_base = torch.cat((pos_cube_1_base, pos_cube_2_base, pos_cube_3_base), dim=1) + quat_cubes_base = torch.cat((quat_cube_1_base, quat_cube_2_base, quat_cube_3_base), dim=1) + + if return_key == "pos": + return pos_cubes_base + elif return_key == "quat": + return quat_cubes_base + elif return_key is None: + return torch.cat((pos_cubes_base, quat_cubes_base), dim=1) + + +def object_abs_obs_in_base_frame( + env: ManagerBasedRLEnv, + cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), + cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), + cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), + ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +): + """ + Object Abs observations (in base frame): remove the relative observations, and add abs gripper pos and quat in robot base frame + cube_1 pos, + cube_1 quat, + cube_2 pos, + cube_2 quat, + cube_3 pos, + cube_3 quat, + gripper pos, + gripper quat, + """ + cube_1: RigidObject = env.scene[cube_1_cfg.name] + cube_2: RigidObject = env.scene[cube_2_cfg.name] + cube_3: RigidObject = env.scene[cube_3_cfg.name] + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + robot: Articulation = env.scene[robot_cfg.name] + + root_pos_w = robot.data.root_pos_w + root_quat_w = robot.data.root_quat_w + + cube_1_pos_w = cube_1.data.root_pos_w + cube_1_quat_w = cube_1.data.root_quat_w + + cube_2_pos_w = cube_2.data.root_pos_w + cube_2_quat_w = cube_2.data.root_quat_w + + cube_3_pos_w = cube_3.data.root_pos_w + cube_3_quat_w = cube_3.data.root_quat_w + + pos_cube_1_base, quat_cube_1_base = math_utils.subtract_frame_transforms( + root_pos_w, root_quat_w, cube_1_pos_w, cube_1_quat_w + ) + pos_cube_2_base, quat_cube_2_base = math_utils.subtract_frame_transforms( + root_pos_w, root_quat_w, cube_2_pos_w, cube_2_quat_w + ) + pos_cube_3_base, quat_cube_3_base = math_utils.subtract_frame_transforms( + root_pos_w, root_quat_w, cube_3_pos_w, cube_3_quat_w + ) + + ee_pos_w = ee_frame.data.target_pos_w[:, 0, :] + ee_quat_w = ee_frame.data.target_quat_w[:, 0, :] + ee_pos_base, ee_quat_base = math_utils.subtract_frame_transforms(root_pos_w, root_quat_w, ee_pos_w, ee_quat_w) + + return torch.cat( + ( + pos_cube_1_base, + quat_cube_1_base, + pos_cube_2_base, + quat_cube_2_base, + pos_cube_3_base, + quat_cube_3_base, + ee_pos_base, + ee_quat_base, + ), + dim=1, + ) + + +def ee_frame_pose_in_base_frame( + env: ManagerBasedRLEnv, + ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + return_key: Literal["pos", "quat", None] = None, +) -> torch.Tensor: + """ + The end effector pose in the robot base frame. + """ + ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] + ee_frame_pos_w = ee_frame.data.target_pos_w[:, 0, :] + ee_frame_quat_w = ee_frame.data.target_quat_w[:, 0, :] + + robot: Articulation = env.scene[robot_cfg.name] + root_pos_w = robot.data.root_pos_w + root_quat_w = robot.data.root_quat_w + ee_pos_in_base, ee_quat_in_base = math_utils.subtract_frame_transforms( + root_pos_w, root_quat_w, ee_frame_pos_w, ee_frame_quat_w + ) + + if return_key == "pos": + return ee_pos_in_base + elif return_key == "quat": + return ee_quat_in_base + elif return_key is None: + return torch.cat((ee_pos_in_base, ee_quat_in_base), dim=1) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/terminations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/terminations.py new file mode 100644 index 000000000000..e306f9eb4a0a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/terminations.py @@ -0,0 +1,92 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Common functions that can be used to activate certain terminations for the lift task. + +The functions can be passed to the :class:`isaaclab.managers.TerminationTermCfg` object to enable +the termination introduced by the function. +""" + +from __future__ import annotations + +import torch +from typing import TYPE_CHECKING + +from isaaclab.assets import Articulation, RigidObject +from isaaclab.managers import SceneEntityCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedRLEnv + + +def cubes_stacked( + env: ManagerBasedRLEnv, + robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), + cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), + cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), + xy_threshold: float = 0.04, + height_threshold: float = 0.005, + height_diff: float = 0.0468, + atol=0.0001, + rtol=0.0001, +): + robot: Articulation = env.scene[robot_cfg.name] + cube_1: RigidObject = env.scene[cube_1_cfg.name] + cube_2: RigidObject = env.scene[cube_2_cfg.name] + cube_3: RigidObject = env.scene[cube_3_cfg.name] + + pos_diff_c12 = cube_1.data.root_pos_w - cube_2.data.root_pos_w + pos_diff_c23 = cube_2.data.root_pos_w - cube_3.data.root_pos_w + + # Compute cube position difference in x-y plane + xy_dist_c12 = torch.norm(pos_diff_c12[:, :2], dim=1) + xy_dist_c23 = torch.norm(pos_diff_c23[:, :2], dim=1) + + # Compute cube height difference + h_dist_c12 = torch.norm(pos_diff_c12[:, 2:], dim=1) + h_dist_c23 = torch.norm(pos_diff_c23[:, 2:], dim=1) + + # Check cube positions + stacked = torch.logical_and(xy_dist_c12 < xy_threshold, xy_dist_c23 < xy_threshold) + stacked = torch.logical_and(h_dist_c12 - height_diff < height_threshold, stacked) + stacked = torch.logical_and(pos_diff_c12[:, 2] < 0.0, stacked) + stacked = torch.logical_and(h_dist_c23 - height_diff < height_threshold, stacked) + stacked = torch.logical_and(pos_diff_c23[:, 2] < 0.0, stacked) + + # Check gripper positions + if hasattr(env.scene, "surface_grippers") and len(env.scene.surface_grippers) > 0: + surface_gripper = env.scene.surface_grippers["surface_gripper"] + suction_cup_status = surface_gripper.state.view(-1, 1) # 1: closed, 0: closing, -1: open + suction_cup_is_open = (suction_cup_status == -1).to(torch.float32) + stacked = torch.logical_and(suction_cup_is_open, stacked) + + else: + if hasattr(env.cfg, "gripper_joint_names"): + gripper_joint_ids, _ = robot.find_joints(env.cfg.gripper_joint_names) + assert len(gripper_joint_ids) == 2, "Terminations only support parallel gripper for now" + + stacked = torch.logical_and( + torch.isclose( + robot.data.joint_pos[:, gripper_joint_ids[0]], + torch.tensor(env.cfg.gripper_open_val, dtype=torch.float32).to(env.device), + atol=atol, + rtol=rtol, + ), + stacked, + ) + stacked = torch.logical_and( + torch.isclose( + robot.data.joint_pos[:, gripper_joint_ids[1]], + torch.tensor(env.cfg.gripper_open_val, dtype=torch.float32).to(env.device), + atol=atol, + rtol=rtol, + ), + stacked, + ) + else: + raise ValueError("No gripper_joint_names found in environment config") + + return stacked diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/stack_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/stack_env_cfg.py new file mode 100644 index 000000000000..3af83de3a19a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/stack_env_cfg.py @@ -0,0 +1,199 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.devices.openxr import XrCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import FrameTransformerCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from . import mdp + + +## +# Scene definition +## +@configclass +class ObjectTableSceneCfg(InteractiveSceneCfg): + """Configuration for the lift scene with a robot and a object. + This is the abstract base implementation, the exact scene is defined in the derived classes + which need to set the target object, robot and end-effector frames + """ + + # robots: will be populated by agent env cfg + robot: ArticulationCfg = MISSING + # end-effector sensor: will be populated by agent env cfg + ee_frame: FrameTransformerCfg = MISSING + + # Table + table = AssetBaseCfg( + prim_path="{ENV_REGEX_NS}/Table", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.5, 0, 0], rot=[0.707, 0, 0, 0.707]), + spawn=UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd"), + ) + + # plane + plane = AssetBaseCfg( + prim_path="/World/GroundPlane", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0, 0, -1.05]), + spawn=GroundPlaneCfg(), + ) + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# MDP settings +## +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + # will be set by agent env cfg + arm_action: mdp.JointPositionActionCfg = MISSING + gripper_action: mdp.BinaryJointPositionActionCfg = MISSING + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + object = ObsTerm(func=mdp.object_obs) + cube_positions = ObsTerm(func=mdp.cube_positions_in_world_frame) + cube_orientations = ObsTerm(func=mdp.cube_orientations_in_world_frame) + eef_pos = ObsTerm(func=mdp.ee_frame_pos) + eef_quat = ObsTerm(func=mdp.ee_frame_quat) + gripper_pos = ObsTerm(func=mdp.gripper_pos) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class RGBCameraPolicyCfg(ObsGroup): + """Observations for policy group with RGB images.""" + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + @configclass + class SubtaskCfg(ObsGroup): + """Observations for subtask group.""" + + grasp_1 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_2"), + }, + ) + stack_1 = ObsTerm( + func=mdp.object_stacked, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "upper_object_cfg": SceneEntityCfg("cube_2"), + "lower_object_cfg": SceneEntityCfg("cube_1"), + }, + ) + grasp_2 = ObsTerm( + func=mdp.object_grasped, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "ee_frame_cfg": SceneEntityCfg("ee_frame"), + "object_cfg": SceneEntityCfg("cube_3"), + }, + ) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + rgb_camera: RGBCameraPolicyCfg = RGBCameraPolicyCfg() + subtask_terms: SubtaskCfg = SubtaskCfg() + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + cube_1_dropping = DoneTerm( + func=mdp.root_height_below_minimum, params={"minimum_height": -0.05, "asset_cfg": SceneEntityCfg("cube_1")} + ) + + cube_2_dropping = DoneTerm( + func=mdp.root_height_below_minimum, params={"minimum_height": -0.05, "asset_cfg": SceneEntityCfg("cube_2")} + ) + + cube_3_dropping = DoneTerm( + func=mdp.root_height_below_minimum, params={"minimum_height": -0.05, "asset_cfg": SceneEntityCfg("cube_3")} + ) + + success = DoneTerm(func=mdp.cubes_stacked) + + +@configclass +class StackEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the stacking environment.""" + + # Scene settings + scene: ObjectTableSceneCfg = ObjectTableSceneCfg(num_envs=4096, env_spacing=2.5, replicate_physics=False) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + # MDP settings + terminations: TerminationsCfg = TerminationsCfg() + + # Unused managers + commands = None + rewards = None + events = None + curriculum = None + + xr: XrCfg = XrCfg( + anchor_pos=(-0.1, -0.5, -1.05), + anchor_rot=(0.866, 0, 0, -0.5), + ) + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 5 + self.episode_length_s = 60.0 + # simulation settings + self.sim.dt = 0.01 # 100Hz + self.sim.render_interval = 2 + + self.sim.physx.bounce_threshold_velocity = 0.2 + self.sim.physx.bounce_threshold_velocity = 0.01 + #self.sim.physx.gpu_found_lost_aggregate_pairs_capacity = 1024 * 1024 * 4 + #self.sim.physx.gpu_total_aggregate_pairs_capacity = 16 * 1024 + self.sim.physx.friction_correlation_distance = 0.00625 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/stack_instance_randomize_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/stack_instance_randomize_env_cfg.py new file mode 100644 index 000000000000..8ef135c4a082 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/stack_instance_randomize_env_cfg.py @@ -0,0 +1,135 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import MISSING + +import isaaclab.sim as sim_utils +from isaaclab.assets import ArticulationCfg, AssetBaseCfg +from isaaclab.envs import ManagerBasedRLEnvCfg +from isaaclab.managers import ObservationGroupCfg as ObsGroup +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import TerminationTermCfg as DoneTerm +from isaaclab.scene import InteractiveSceneCfg +from isaaclab.sensors.frame_transformer.frame_transformer_cfg import FrameTransformerCfg +from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg +from isaaclab.utils import configclass +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR + +from . import mdp + + +## +# Scene definition +## +@configclass +class ObjectTableSceneCfg(InteractiveSceneCfg): + """Configuration for the lift scene with a robot and a object. + This is the abstract base implementation, the exact scene is defined in the derived classes + which need to set the target object, robot and end-effector frames + """ + + # robots: will be populated by agent env cfg + robot: ArticulationCfg = MISSING + # end-effector sensor: will be populated by agent env cfg + ee_frame: FrameTransformerCfg = MISSING + + # Table + table = AssetBaseCfg( + prim_path="{ENV_REGEX_NS}/Table", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0.5, 0, 0], rot=[0.707, 0, 0, 0.707]), + spawn=UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd"), + ) + + # plane + plane = AssetBaseCfg( + prim_path="/World/GroundPlane", + init_state=AssetBaseCfg.InitialStateCfg(pos=[0, 0, -1.05]), + spawn=GroundPlaneCfg(), + ) + + # lights + light = AssetBaseCfg( + prim_path="/World/light", + spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), + ) + + +## +# MDP settings +## +@configclass +class ActionsCfg: + """Action specifications for the MDP.""" + + # will be set by agent env cfg + arm_action: mdp.JointPositionActionCfg = MISSING + gripper_action: mdp.BinaryJointPositionActionCfg = MISSING + + +@configclass +class ObservationsCfg: + """Observation specifications for the MDP.""" + + @configclass + class PolicyCfg(ObsGroup): + """Observations for policy group with state values.""" + + actions = ObsTerm(func=mdp.last_action) + joint_pos = ObsTerm(func=mdp.joint_pos_rel) + joint_vel = ObsTerm(func=mdp.joint_vel_rel) + object = ObsTerm(func=mdp.instance_randomize_object_obs) + cube_positions = ObsTerm(func=mdp.instance_randomize_cube_positions_in_world_frame) + cube_orientations = ObsTerm(func=mdp.instance_randomize_cube_orientations_in_world_frame) + eef_pos = ObsTerm(func=mdp.ee_frame_pos) + eef_quat = ObsTerm(func=mdp.ee_frame_quat) + gripper_pos = ObsTerm(func=mdp.gripper_pos) + + def __post_init__(self): + self.enable_corruption = False + self.concatenate_terms = False + + # observation groups + policy: PolicyCfg = PolicyCfg() + + +@configclass +class TerminationsCfg: + """Termination terms for the MDP.""" + + time_out = DoneTerm(func=mdp.time_out, time_out=True) + + +@configclass +class StackInstanceRandomizeEnvCfg(ManagerBasedRLEnvCfg): + """Configuration for the stacking environment.""" + + # Scene settings + scene: ObjectTableSceneCfg = ObjectTableSceneCfg(num_envs=4096, env_spacing=2.5, replicate_physics=False) + # Basic settings + observations: ObservationsCfg = ObservationsCfg() + actions: ActionsCfg = ActionsCfg() + # MDP settings + terminations: TerminationsCfg = TerminationsCfg() + + # Unused managers + commands = None + rewards = None + events = None + curriculum = None + + def __post_init__(self): + """Post initialization.""" + # general settings + self.decimation = 5 + self.episode_length_s = 30.0 + # simulation settings + self.sim.dt = 0.01 # 100Hz + self.sim.render_interval = self.decimation + + self.sim.physx.bounce_threshold_velocity = 0.2 + self.sim.physx.bounce_threshold_velocity = 0.01 + self.sim.physx.gpu_found_lost_aggregate_pairs_capacity = 1024 * 1024 * 4 + self.sim.physx.gpu_total_aggregate_pairs_capacity = 16 * 1024 + self.sim.physx.friction_correlation_distance = 0.00625 From 03791a6e868ae0cdeea2aeab0f6858a5e42eed93 Mon Sep 17 00:00:00 2001 From: LJones-alt Date: Wed, 14 Jan 2026 16:40:17 +0000 Subject: [PATCH 11/15] now with automated experiment running --- .vscode/browse.vc.db | Bin 0 -> 688128 bytes .vscode/browse.vc.db-shm | Bin 0 -> 32768 bytes .vscode/browse.vc.db-wal | 0 docker/Dockerfile.base | 18 +- docs/find_best_checkpoints.py | 160 ++++ docs/run_experiments.sh | 66 ++ .../isaaclab_mimic/annotate_demos.py | 4 + scripts/imitation_learning/robomimic/play.py | 2 + .../robomimic/play_diffusion.py | 297 +++++++ .../robomimic/play_ensemble_v05.py | 781 ++++++++++++++++++ scripts/imitation_learning/robomimic/train.py | 88 +- .../robomimic/train_ensemble.py | 55 +- scripts/tools/check_demos.py | 38 + scripts/tools/copy_metadata_hdf5.py | 3 +- .../isaaclab/managers/recorder_manager.py | 3 +- .../isaaclab/safety/switchingLogic.py | 2 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zvTEavQl6xTTP74{`j}8VE{Oe0BY^Dhs}Zcb1u>ZIn--pj)JtUqORpdC zKd3Tg>Bel97nuk;2v7emYpa${z0<=g*UP%t zrgoq0+qP}ib5={uwB-vHzxmB(m1(N_2i1P#ORIiXwW0D&hTj@?=|8Qnhib}?A^}AL ziUbr1C=yU4ph!TGfFc1!0*V9_2`Ca!B%nwjM+rQaadG89q5078WF?(<}$7zx*$JEo9tbzKH z|3!iN|21bHIzf7V9VmzSZj4L65pL;DO;Qujl5&Sq z-GlxGfC!8>r(YDdHo*AKD{X48 zWxYk;lSh7g%+t5|<^K8U;p~0%);sC; z$GpFoSFYd6D$kYw?bvjpR-y(`2T_+%S5c8GEk{+aoyYM-RC!PJT5-PUD`&>mB0zuu z0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+009C72oNAZ zfB*pk1PBlyK!5-N0t5&U$WmZ2%Tz)Un1w(q?aksbo Optional[float]: + """ + Extract validation loss from checkpoint filename. + + Expected format: model_epoch_XXX_best_validation_YYY.YYY.pth + + Args: + filename: The checkpoint filename + + Returns: + The validation loss as a float, or None if parsing fails + """ + pattern = r'model_epoch_\d+_best_validation_([\d.]+)\.pth' + match = re.search(pattern, filename) + if match: + return float(match.group(1)) + return None + + +def find_best_checkpoint(model_dir: Path) -> Optional[Tuple[Path, float]]: + """ + Find the checkpoint with the lowest validation loss in a model directory. + + Args: + model_dir: Path to the model directory (e.g., model0, model1, etc.) + + Returns: + Tuple of (checkpoint_path, validation_loss) or None if no checkpoints found + """ + # Search for all checkpoint files in the models subdirectory + checkpoint_pattern = "model_epoch_*_best_validation_*.pth" + checkpoints = list(model_dir.rglob(checkpoint_pattern)) + + if not checkpoints: + print(f" ⚠️ No checkpoints found in {model_dir.name}") + return None + + best_checkpoint = None + best_loss = float('inf') + + for checkpoint in checkpoints: + loss = parse_validation_loss(checkpoint.name) + if loss is not None and loss < best_loss: + best_loss = loss + best_checkpoint = checkpoint + + if best_checkpoint: + return (best_checkpoint, best_loss) + + print(f" ⚠️ Could not parse validation loss from checkpoints in {model_dir.name}") + return None + + +def main(): + """Main function to process all model directories.""" + # Define paths - handle both host and Docker container paths + script_dir = Path(__file__).parent.resolve() + base_dir = script_dir / "place/Dev-IK-Rel-Place-v0" + output_dir = base_dir / "best_models" + + # Create output directory (including parent directories if needed) + output_dir.mkdir(parents=True, exist_ok=True) + print(f"📁 Output directory: {output_dir}\n") + + # Find all model directories (model0, model1, ..., modelN) + model_dirs = sorted([d for d in base_dir.iterdir() + if d.is_dir() and d.name.startswith("model") + and d.name != "best_models"]) + + if not model_dirs: + print("❌ No model directories found!") + return + + print(f"Found {len(model_dirs)} model directories\n") + print("=" * 80) + + # Process each model directory + results = [] + for model_dir in model_dirs: + print(f"\n🔍 Processing {model_dir.name}...") + + result = find_best_checkpoint(model_dir) + if result: + checkpoint_path, validation_loss = result + print(f" ✅ Best checkpoint: {checkpoint_path.name}") + print(f" 📊 Validation loss: {validation_loss}") + + # Copy to output directory with model name prefix + output_filename = f"{model_dir.name}_{checkpoint_path.name}" + output_path = output_dir / output_filename + + shutil.copy2(checkpoint_path, output_path) + print(f" 📋 Copied to: {output_filename}") + + results.append({ + 'model': model_dir.name, + 'checkpoint': checkpoint_path.name, + 'loss': validation_loss, + 'output': output_filename + }) + + # Print summary + print("\n" + "=" * 80) + print("\nSUMMARY") + print("=" * 80) + print(f"\nProcessed {len(model_dirs)} models") + print(f"Successfully copied {len(results)} best checkpoints\n") + + if results: + print("Best checkpoints by validation loss:") + print("-" * 80) + sorted_results = sorted(results, key=lambda x: x['loss']) + for i, r in enumerate(sorted_results, 1): + print(f"{i:2d}. {r['model']:8s} | Loss: {r['loss']:12.2f} | {r['checkpoint']}") + + # Save file paths to text file + save_paths_to_file(output_dir, results) + + print(f"\nAll best models saved to: {output_dir}") + else: + print("No checkpoints were copied") + + +def save_paths_to_file(output_dir: Path, results: list) -> None: + """ + Save all copied checkpoint file paths to a text file. + + Args: + output_dir: Directory where the checkpoints were copied + results: List of dictionaries containing model information + """ + txt_file = output_dir / "best_model_paths.txt" + + with open(txt_file, 'w') as f: + for result in sorted(results, key=lambda x: x['model']): + file_path = output_dir / result['output'] + f.write(f"{file_path}\n") + + print(f"📝 File paths saved to: {txt_file}") + + +if __name__ == "__main__": + main() diff --git a/docs/run_experiments.sh b/docs/run_experiments.sh new file mode 100644 index 000000000000..9f7da360e12a --- /dev/null +++ b/docs/run_experiments.sh @@ -0,0 +1,66 @@ + + +## script to run experiments in the background + +task="Dev-IK-Rel-Place-v0" +horizon=5000 +num_rollouts=100 +run_file="scripts/imitation_learning/robomimic/play_ensemble_v05.py" +ensemble_size=15 + +##### this now runs the experiments + +## ensemble 15 +seed=101 +exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" +./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless + +seed=107 +exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" +./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless + +seed=115 +exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" +./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless + +## ensemble 10 +ensemble_size=10 +seed=101 +exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" +./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless + +seed=107 +exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" +./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless + +seed=115 +exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" +./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless + +## ensemble 5 +ensemble_size=5 +seed=101 +exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" +./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless + +seed=107 +exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" +./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless + +seed=115 +exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" +./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless + +## ensemble 1 +ensemble_size=1 +seed=101 +exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" +./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless + +seed=107 +exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" +./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless + +seed=115 +exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" +./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless diff --git a/scripts/imitation_learning/isaaclab_mimic/annotate_demos.py b/scripts/imitation_learning/isaaclab_mimic/annotate_demos.py index 17322c6e93ce..489a52bbc976 100644 --- a/scripts/imitation_learning/isaaclab_mimic/annotate_demos.py +++ b/scripts/imitation_learning/isaaclab_mimic/annotate_demos.py @@ -210,6 +210,10 @@ def main(): # Disable all termination terms env_cfg.terminations = None + # Note: We keep events enabled as the reset_to() function applies state AFTER events run, + # so the recorded state should override any randomization. If object placement is still + # inaccurate, try using --device cuda:0 instead of --device cpu. + # Set up recorder terms for mimic annotations env_cfg.recorders = MimicRecorderManagerCfg() if not args_cli.auto: diff --git a/scripts/imitation_learning/robomimic/play.py b/scripts/imitation_learning/robomimic/play.py index 4b1476f6bea1..02d293b21eba 100644 --- a/scripts/imitation_learning/robomimic/play.py +++ b/scripts/imitation_learning/robomimic/play.py @@ -178,6 +178,8 @@ def main(): results.append(terminated) print(f"[INFO] Trial {trial}: {terminated}\n") + print(f"\n Environment summary : {env}") + # Print results print(f"\nSuccessful trials: {results.count(True)}, out of {len(results)} trials") print(f"Success rate: {results.count(True) / len(results)}") print(f"Trial Results: {results}\n") diff --git a/scripts/imitation_learning/robomimic/play_diffusion.py b/scripts/imitation_learning/robomimic/play_diffusion.py new file mode 100644 index 000000000000..15d78ca345f7 --- /dev/null +++ b/scripts/imitation_learning/robomimic/play_diffusion.py @@ -0,0 +1,297 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +"""Script to play and evaluate a trained Diffusion Policy from robomimic. + +This script loads a robomimic diffusion policy and plays it in an Isaac Lab environment. +It handles the temporal observation stacking required by diffusion policy. + +Args: + task: Name of the environment. + checkpoint: Path to the robomimic policy checkpoint. + horizon: If provided, override the step horizon of each rollout. + num_rollouts: If provided, override the number of rollouts. + seed: If provided, override the default random seed. + norm_factor_min: If provided, minimum value of the action space normalization factor. + norm_factor_max: If provided, maximum value of the action space normalization factor. +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Evaluate robomimic diffusion policy for Isaac Lab environment.") +parser.add_argument( + "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations." +) +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument("--checkpoint", type=str, default=None, help="Pytorch model checkpoint to load.") +parser.add_argument("--horizon", type=int, default=800, help="Step horizon of each rollout.") +parser.add_argument("--num_rollouts", type=int, default=1, help="Number of rollouts.") +parser.add_argument("--seed", type=int, default=101, help="Random seed.") +parser.add_argument( + "--norm_factor_min", type=float, default=None, help="Optional: minimum value of the normalization factor." +) +parser.add_argument( + "--norm_factor_max", type=float, default=None, help="Optional: maximum value of the normalization factor." +) +parser.add_argument("--enable_pinocchio", default=False, action="store_true", help="Enable Pinocchio.") + + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +if args_cli.enable_pinocchio: + # Import pinocchio before AppLauncher to force the use of the version installed by IsaacLab and not the one installed by Isaac Sim + # pinocchio is required by the Pink IK controllers and the GR1T2 retargeter + import pinocchio # noqa: F401 + +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import copy +from collections import deque +import gymnasium as gym +import numpy as np +import random +import torch + +import robomimic.utils.file_utils as FileUtils +import robomimic.utils.torch_utils as TorchUtils + +if args_cli.enable_pinocchio: + import isaaclab_tasks.manager_based.manipulation.pick_place # noqa: F401 + +from isaaclab_tasks.utils import parse_env_cfg + + +def get_observation_horizon(policy): + """Get the observation horizon from diffusion policy config. + + Args: + policy: The loaded robomimic policy wrapper. + + Returns: + int: The observation horizon (default 2 for diffusion policy). + """ + if hasattr(policy.policy, 'algo_config') and hasattr(policy.policy.algo_config, 'horizon'): + return policy.policy.algo_config.horizon.observation_horizon + # Default for diffusion policy if not found + return 2 + + +def prepare_obs_for_diffusion(obs_dict, obs_history): + """Prepare observations for diffusion policy inference. + + Diffusion policy expects observations with shape [B, T, D] where: + - B = batch size (1 for single env inference) + - T = observation_horizon (temporal dimension) + - D = observation feature dimension + + Args: + obs_dict: Current observation dictionary from environment + obs_history: Deque of past observations + + Returns: + Stacked observation dictionary with temporal dimension + """ + # Store current observation (without batch dimension) + current_obs = {} + for k in obs_dict: + if obs_dict[k].dim() > 1: + current_obs[k] = obs_dict[k].squeeze(0) + else: + current_obs[k] = obs_dict[k] + + obs_history.append(current_obs) + + # Stack observations along temporal dimension: [T, D] -> [1, T, D] + stacked_obs = {} + for k in obs_dict: + obs_list = [obs_history[i][k] for i in range(len(obs_history))] + stacked = torch.stack(obs_list, dim=0).unsqueeze(0) # [1, T, D] + stacked_obs[k] = stacked + + # Debug: print shapes on first call + if not hasattr(prepare_obs_for_diffusion, '_printed'): + print("\n[DEBUG] Observation shapes after stacking:") + for k, v in stacked_obs.items(): + print(f" {k}: {v.shape} (ndim={v.ndim})") + prepare_obs_for_diffusion._printed = True + + return stacked_obs + + +def rollout(policy, env, success_term, horizon, device): + """Perform a single rollout of the diffusion policy in the environment. + + Args: + policy: The robomimic diffusion policy to play. + env: The environment to play in. + horizon: The step horizon of each rollout. + device: The device to run the policy on. + + Returns: + terminated: Whether the rollout terminated successfully. + traj: The trajectory of the rollout. + """ + policy.start_episode() + obs_dict, _ = env.reset() + traj = dict(actions=[], obs=[], next_obs=[]) + + # Setup temporal observation history for diffusion policy + observation_horizon = get_observation_horizon(policy) + obs_history = deque(maxlen=observation_horizon) + + # Initialize history with first observation (repeated to fill the queue) + first_obs = {} + for k in obs_dict["policy"]: + if obs_dict["policy"][k].dim() > 1: + first_obs[k] = obs_dict["policy"][k].squeeze(0) + else: + first_obs[k] = obs_dict["policy"][k] + + # Fill history with copies of first observation + for _ in range(observation_horizon): + obs_history.append(copy.deepcopy(first_obs)) + + for i in range(horizon): + # Prepare observations with temporal stacking for diffusion policy + obs = prepare_obs_for_diffusion(obs_dict["policy"], obs_history) + + # Debug: print gripper observation at key steps + # if i == 0 or i == 5: + # print(f"\n[DEBUG] Step {i} - Observations:") + # print(f" gripper_pos: {obs['gripper_pos'][0, -1, :].cpu().numpy()}") # Last timestep + # print(f" eef_pos: {obs['eef_pos'][0, -1, :].cpu().numpy()}") + # print(f" object_position: {obs['object_position'][0, -1, :].cpu().numpy()}") + + # Check if environment has image observations + if hasattr(env.cfg, "image_obs_list"): + # Process image observations for robomimic inference + for image_name in env.cfg.image_obs_list: + if image_name in obs_dict["policy"].keys(): + # Convert from chw uint8 to hwc normalized float + image = torch.squeeze(obs_dict["policy"][image_name]) + image = image.permute(2, 0, 1).clone().float() + image = image / 255.0 + image = image.clip(0.0, 1.0) + # Stack image observations temporally as well + obs[image_name] = image.unsqueeze(0).unsqueeze(0).expand(1, observation_horizon, -1, -1, -1) + + traj["obs"].append(obs) + + # Compute actions from diffusion policy + # Use batched_ob=True because we already have batch dimension [1, T, D] + actions = policy(obs, batched_ob=True) + + # Debug: print action stats periodically + # if i == 0 or i == 10 or i == 50: + # print(f"\n[DEBUG] Step {i} - Raw policy actions:") + # print(f" Shape: {actions.shape}") + # print(f" Min: {actions.min():.4f}, Max: {actions.max():.4f}") + # print(f" Values: {actions}") + # # Check if policy has normalization stats + # if hasattr(policy, 'action_normalization_stats') and policy.action_normalization_stats is not None: + # print(f" Action norm stats available: {policy.action_normalization_stats.keys()}") + # else: + # print(f" WARNING: No action normalization stats in policy!") + + # Unnormalize actions if normalization factors provided + if args_cli.norm_factor_min is not None and args_cli.norm_factor_max is not None: + actions = ( + (actions + 1) * (args_cli.norm_factor_max - args_cli.norm_factor_min) + ) / 2 + args_cli.norm_factor_min + + actions = torch.from_numpy(actions).to(device=device).view(1, env.action_space.shape[1]) + + # Apply actions to environment + obs_dict, _, terminated, truncated, _ = env.step(actions) + + # Record trajectory + traj["actions"].append(actions.tolist()) + traj["next_obs"].append(obs_dict["policy"]) + + # Check if rollout was successful + if bool(success_term.func(env, **success_term.params)[0]): + return True, traj + elif terminated or truncated: + return False, traj + + return False, traj + + +def main(): + """Run a trained diffusion policy from robomimic with Isaac Lab environment.""" + # parse configuration + env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=1, use_fabric=not args_cli.disable_fabric) + + # Set observations to dictionary mode for Robomimic + env_cfg.observations.policy.concatenate_terms = False + + # Set termination conditions + env_cfg.terminations.time_out = None + + # Disable recorder + env_cfg.recorders = None + + # Extract success checking function + success_term = env_cfg.terminations.success + env_cfg.terminations.success = None + + # Create environment + env = gym.make(args_cli.task, cfg=env_cfg).unwrapped + + # Set seed for reproducibility + torch.manual_seed(args_cli.seed) + np.random.seed(args_cli.seed) + random.seed(args_cli.seed) + env.seed(args_cli.seed) + + # Acquire device + device = TorchUtils.get_torch_device(try_to_use_cuda=True) + + # Load policy once (diffusion policy maintains internal action queue) + policy, _ = FileUtils.policy_from_checkpoint(ckpt_path=args_cli.checkpoint, device=device) + print(f"[INFO] Loaded diffusion policy from {args_cli.checkpoint}") + + # Debug: print expected observation shapes from policy + print("\n[DEBUG] Policy expected obs_shapes:") + for k, v in policy.policy.obs_shapes.items(): + print(f" {k}: {v} (len={len(v)})") + + # Get and print observation horizon + obs_horizon = get_observation_horizon(policy) + print(f"[INFO] Diffusion policy observation_horizon: {obs_horizon}") + + # Run policy rollouts + results = [] + for trial in range(args_cli.num_rollouts): + print(f"[INFO] Starting trial {trial}") + terminated, traj = rollout(policy, env, success_term, args_cli.horizon, device) + results.append(terminated) + print(f"[INFO] Trial {trial}: {'Success' if terminated else 'Failed'}\n") + + print(f"\nSuccessful trials: {results.count(True)}, out of {len(results)} trials") + print(f"Success rate: {results.count(True) / len(results):.2%}") + print(f"Trial Results: {results}\n") + + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/imitation_learning/robomimic/play_ensemble_v05.py b/scripts/imitation_learning/robomimic/play_ensemble_v05.py new file mode 100644 index 000000000000..43771bc8d2ac --- /dev/null +++ b/scripts/imitation_learning/robomimic/play_ensemble_v05.py @@ -0,0 +1,781 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# run this script: +# ./isaaclab.sh -p scripts/imitation_learning/robomimic/play_ensemble_v05.py --device cuda --task Dev-IK-Rel-v0 --num_rollouts 100 +# stack cube task: ./isaaclab.sh -p scripts/imitation_learning/robomimic/play.py --device cuda --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --num_rollouts 50 +"""Script to play and evaluate a trained policy from robomimic. + +This script loads a robomimic policy and plays it in an Isaac Lab environment. +Updated for robomimic v0.5 compatibility. + +Args: + task: Name of the environment. + checkpoint: Path to the robomimic policy checkpoint. + horizon: If provided, override the step horizon of each rollout. + num_rollouts: If provided, override the number of rollouts. + seed: If provided, overeride the default random seed. + norm_factor_min: If provided, minimum value of the action space normalization factor. + norm_factor_max: If provided, maximum value of the action space normalization factor. +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Evaluate robomimic policy for Isaac Lab environment.") +parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.") +parser.add_argument("--video_length", type=int, default=800, help="Length of the recorded video (in steps).") +parser.add_argument( + "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations." +) +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument("--checkpoint", type=str, default=None, help="Pytorch model checkpoint to load.") +parser.add_argument("--horizon", type=int, default=2000, help="Step horizon of each rollout.") +parser.add_argument("--num_rollouts", type=int, default=1, help="Number of rollouts.") +parser.add_argument("--seed", type=int, default=101, help="Random seed.") +parser.add_argument( + "--norm_factor_min", type=float, default=None, help="Optional: minimum value of the normalization factor." +) +parser.add_argument( + "--norm_factor_max", type=float, default=None, help="Optional: maximum value of the normalization factor." +) +parser.add_argument("--enable_pinocchio", default=False, action="store_true", help="Enable Pinocchio.") + +parser.add_argument("--model_name", type=str, default=None) +parser.add_argument("--exp_name", type=str, default=None) + +group = parser.add_mutually_exclusive_group() +group.add_argument("--use_recovery", type=bool ,default= False, help="Enable recovery mechanism. By default recovery is enabled.") + +parser.add_argument("--ensemble_size", type=int, default=10) + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +if args_cli.enable_pinocchio: + # Import pinocchio before AppLauncher to force the use of the version installed by IsaacLab and not the one installed by Isaac Sim + # pinocchio is required by the Pink IK controllers and the GR1T2 retargeter + import pinocchio # noqa: F401 +import numpy as np +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import time +import copy +import random +import os +import gymnasium as gym +import torch +import math +from collections import deque + +import robomimic.utils.file_utils as FileUtils +import robomimic.utils.torch_utils as TorchUtils +from isaaclab_tasks.manager_based.manipulation.cube_lift.mdp.observations import get_joint_pos, object_grasped +#from chills.tasks.mdp.observations import get_joint_pos, object_grasped +if args_cli.enable_pinocchio: + import isaaclab_tasks.manager_based.manipulation.pick_place # noqa: F401 + +from isaaclab_tasks.utils import parse_env_cfg +#from isaaclab.utils.logging_helper import LoggingHelper, ErrorType, LogType +from isaaclab.utils.math import matrix_from_quat, convert_quat, quat_mul, euler_xyz_from_quat +from evaluation import ensemble_uncertainty +from isaaclab.safety.switchingLogic import SwitchingLogic +from isaaclab.controllers import DifferentialIKController, DifferentialIKControllerCfg +from isaaclab.utils.math import subtract_frame_transforms, euler_xyz_from_quat +from isaaclab.managers import SceneEntityCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg, BinaryJointPositionActionCfg +from isaaclab.envs.mdp.actions.binary_joint_actions import BinaryJointPositionAction +from backup_controller_handler import BackupController + + +def policy_from_checkpoint_v05(ckpt_path, device, verbose=True): + """ + Load a policy from a checkpoint file - compatible with robomimic v0.5. + + This is a wrapper that handles API differences between robomimic versions. + + Args: + ckpt_path: Path to the checkpoint file. + device: Device to load the policy on. + verbose: Whether to print verbose output. + + Returns: + policy: The loaded policy. + ckpt_dict: The checkpoint dictionary. + """ + import robomimic + from packaging import version + + # Check robomimic version + robomimic_version = getattr(robomimic, '__version__', '0.3.0') + + if verbose: + print(f"[INFO] Loading checkpoint with robomimic version {robomimic_version}") + + # robomimic v0.5+ may have different API + if version.parse(robomimic_version) >= version.parse('0.5.0'): + # v0.5 API - policy_from_checkpoint returns (policy, ckpt_dict) + try: + result = FileUtils.policy_from_checkpoint(ckpt_path=ckpt_path, device=device, verbose=verbose) + if isinstance(result, tuple): + return result + else: + return result, None + except TypeError as e: + if verbose: + print(f"[WARN] policy_from_checkpoint failed with: {e}") + print("[INFO] Trying alternative loading method...") + + # Alternative: manual loading + import torch + import json + from robomimic.algo import algo_factory + from robomimic.config import config_factory + + ckpt_dict = torch.load(ckpt_path, map_location=device) + + # Get config from checkpoint + config = ckpt_dict.get('config', {}) + if isinstance(config, dict): + algo_name = config.get('algo_name', 'bc') + config_obj = config_factory(algo_name) + with config_obj.values_unlocked(): + config_obj.update(config) + config = config_obj + + # Create policy + policy = algo_factory( + algo_name=config.algo_name, + config=config, + obs_key_shapes=ckpt_dict.get('shape_metadata', {}).get('all_shapes', {}), + ac_dim=ckpt_dict.get('shape_metadata', {}).get('ac_dim', 7), + device=device, + ) + + # Load weights + policy.deserialize(ckpt_dict['model']) + policy.set_eval() + + return policy, ckpt_dict + else: + # Older API + return FileUtils.policy_from_checkpoint(ckpt_path=ckpt_path, device=device, verbose=verbose) + + +def rollout(policy, env, success_term, horizon, device): + """Perform a single rollout of the policy in the environment. + + Args: + policy: The robomimicpolicy to play. + env: The environment to play in. + horizon: The step horizon of each rollout. + device: The device to run the policy on. + + Returns: + terminated: Whether the rollout terminated. + traj: The trajectory of the rollout. + """ + policy.start_episode() + obs_dict, _ = env.reset() + + #print(f"obs_dict type: {type(obs_dict)},\nobs_dict contents:\n {obs_dict}") + + traj = dict(actions=[], obs=[], next_obs=[], sub_obs=[], uncertainties=[]) + + # Define the observation keys the trained model expects + TRAINED_OBS_KEYS = ['eef_pos', 'eef_quat', 'gripper_pos', 'joint_pos', 'joint_vel', + 'object_position', 'target_object_position', 'actions'] + + for i in range(horizon): + # Prepare observations + obs = copy.deepcopy(obs_dict["policy"]) + sub_obs = copy.deepcopy(obs_dict["subtask_terms"]) + + for ob in obs: + obs[ob] = torch.squeeze(obs[ob]) + #print(f"found observation : {obs[ob]}") + + for subob in sub_obs: + sub_obs[subob] = torch.squeeze(sub_obs[subob]) + + # Check if environment image observations + if hasattr(env.cfg, "image_obs_list"): + # Process image observations for robomimic inference + for image_name in env.cfg.image_obs_list: + if image_name in obs_dict["policy"].keys(): + # Convert from chw uint8 to hwc normalized float + image = torch.squeeze(obs_dict["policy"][image_name]) + image = image.permute(2, 0, 1).clone().float() + image = image / 255.0 + image = image.clip(0.0, 1.0) + obs[image_name] = image + + traj["obs"].append(obs) + traj["sub_obs"].append(sub_obs) + + # Filter observations to only include keys the model was trained with + policy_obs = {k: obs[k] for k in TRAINED_OBS_KEYS if k in obs} + + # Add dropout layers to the model and calculate uncertainty and remove at the end to not effect final action + + #hooks = inject_dropout_layers(policy=policy, probability=0.1) + # uncertainty_dict = MC_dropout_uncertainty(policy=policy, obs=obs, niters=15) + # traj['uncertainties'].append(uncertainty_dict['variance']) + #remove_dropout_layers(hooks) + + # Compute actions + actions = policy(policy_obs) + + # Unnormalize actions + if args_cli.norm_factor_min is not None and args_cli.norm_factor_max is not None: + actions = ( + (actions + 1) * (args_cli.norm_factor_max - args_cli.norm_factor_min) + ) / 2 + args_cli.norm_factor_min + + actions = torch.from_numpy(actions).to(device=device).view(1, env.action_space.shape[1]) + + # Apply actions + obs_dict, _, terminated, truncated, _ = env.step(actions) + obs = obs_dict["policy"] + sub_obs = obs_dict["subtask_terms"] + + # Record trajectory + traj["actions"].append(actions.tolist()) + traj["next_obs"].append(obs) + + + # Check if rollout was successful + if bool(success_term.func(env, **success_term.params)[0]): + return True, traj + elif terminated or truncated: + return False, traj + + return False, traj + + +# tip over detection +def has_fallen(asset, threshold: float = -0.7, debug: bool = True) -> bool: + """ + Detect if an IsaacLab asset has fallen over by checking its up vector. + + Args: + asset: IsaacLab asset (e.g. robot, object) with .get_world_pose() + threshold: minimum z-component of the up vector (1.0 = perfectly upright). + Typical cutoff ~0.7 (≈45° tilt). + debug: if True, prints internal values for inspection. + + Returns: + True if fallen, False otherwise. + """ + quat = asset.data.body_link_quat_w # (pos, quat), usually torch.Tensors + # print("Beaker quat : ", quat) + # Ensure torch tensor, shape (4,) + if isinstance(quat, (list, tuple)): + quat = torch.tensor(quat, dtype=torch.float32) + quat = quat.view(-1, 4) # shape (1,4) + + # Convert quaternion to wxyz ordering + quat_wxyz = convert_quat(quat, to="wxyz") + + # Rotation matrix (1,3,3) + R = matrix_from_quat(quat_wxyz) + + # World up vector is the third column of rotation matrix + up = R[0, :, 2] # shape (3,) + uprightness = float(up[2]) + + if debug: + print(f"[has_fallen] quat={quat_wxyz.cpu().numpy().round(4)}") + print(f"[has_fallen] up={up.cpu().numpy().round(4)} uprightness={uprightness:.3f}") + + return uprightness > threshold + + +def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, use_recovery=True, rollout_num=0): + """Perform a single rollout of the policy in the environment. + + Args: + policy: The robomimicpolicy to play. + env: The environment to play in. + horizon: The step horizon of each rollout. + device: The device to run the policy on. + + Returns: + terminated: Whether the rollout terminated. + traj: The trajectory of the rollout. + """ + ## do setup + print("running rollout number : ", rollout_num) + for policy in ensemble: + policy.start_episode() + obs_dict, _ = env.reset() + traj = dict(actions=[], obs=[], next_obs=[], sub_obs=[], + uncertainties=[], min_actions=[], max_actions=[], + time_taken=[], joint_pos=[], recovery=[], failure=[], grasp=[], appr=[]) + + ###### SETUP SWITCHING LOGIC #### + switchingLogic = SwitchingLogic(parameters, horizon=horizon) + certain_joint_positions = [] + + ###### SET UP RECOVERY #### + use_recovery=False + recovery_activated_during_rollout = 0 + print("rollout recovery enabled ? : ", use_recovery) + + ##### RECOVERY CONFIG #### + recovery_cooldown_duration = 300 # timesteps + recovery_cooldown_timer = recovery_cooldown_duration + recovery_cooldown_active = False + recovery = [] + recovery_steps = 0 + max_recovery_steps = 500 # Safety limit for recovery mode + + ##### CONFIG RECOVERY CONTROLLER #### + backup_controller = BackupController(env, device) + state_guess = 0 + last_state = 0 + recovery_mode = False + object_knocked = False + + # Define the observation keys the trained model expects + # These must match the keys used during training (from shape_metadata) + TRAINED_OBS_KEYS = ['eef_pos', 'eef_quat', 'gripper_pos', 'joint_pos', 'joint_vel', + 'object_position', 'target_object_position', 'actions'] + + print(f'Running for horizon {horizon}') + for i in range(horizon): + # Prepare observations + + obs = copy.deepcopy(obs_dict["policy"]) + sub_obs = copy.deepcopy(obs_dict["subtask_terms"]) + + for ob in obs: + obs[ob] = torch.squeeze(obs[ob]) + + for subob in sub_obs: + sub_obs[subob] = torch.squeeze(sub_obs[subob]) + traj["obs"].append(obs) + traj["sub_obs"].append(sub_obs) + + # Check if environment image observations + if hasattr(env.cfg, "image_obs_list"): + # Process image observations for robomimic inference + for image_name in env.cfg.image_obs_list: + if image_name in obs_dict["policy"].keys(): + # Convert from chw uint8 to hwc normalized float + image = torch.squeeze(obs_dict["policy"][image_name]) + image = image.permute(2, 0, 1).clone().float() + image = image / 255.0 + image = image.clip(0.0, 1.0) + obs[image_name] = image + + # Filter observations to only include keys the model was trained with + # This prevents dimension mismatch errors when env has extra observations + policy_obs = {k: obs[k] for k in TRAINED_OBS_KEYS if k in obs} + + # Debug: print observation shapes on first iteration + if i == 0: + print(f"[DEBUG] Available obs keys: {list(obs.keys())}") + print(f"[DEBUG] Filtered policy_obs keys: {list(policy_obs.keys())}") + total_dim = 0 + for k, v in policy_obs.items(): + dim = v.shape[-1] if len(v.shape) > 0 else 1 + print(f"[DEBUG] {k}: shape={v.shape}, dim={dim}") + total_dim += dim + print(f"[DEBUG] Total dimension: {total_dim} (expected 44)") + missing = [k for k in TRAINED_OBS_KEYS if k not in obs] + if missing: + print(f"[DEBUG] MISSING keys: {missing}") + + #### get policy action #### + metrics = ensemble_uncertainty(ensemble, policy_obs) + uncertainty = metrics['variance'] + policy_actions = metrics['mean'] + + # Robust state estimation based on actual observations + is_grasping = bool(sub_obs['grasp'].item()) if sub_obs['grasp'].numel() == 1 else bool(torch.any(sub_obs['grasp'])) + + # Update state guess based on actual gripper/object state + if is_grasping: + # Object is grasped - should be in LIFT or later states + if state_guess < 4: # LIFT_OBJECT = 4 + state_guess = 4 + print(f"[STATE] Updated to LIFT_OBJECT (grasp detected)") + else: + # Not grasping - if we thought we were lifting, we dropped it + if state_guess >= 4 and state_guess < 7: # Was in lift/move states + # Object dropped - need to re-approach + state_guess = 0 # Go back to REST to safely re-approach + print(f"[STATE] Reset to REST (grasp lost)") + + sm_actions, state_guess = backup_controller.get_controller_action(state_guess, 0) + if last_state != state_guess: + print(f"[STATE] Transition: {last_state} -> {state_guess}") + last_state = state_guess + + + # this is the switching logic + uncertainty_triggered = switchingLogic.check(uncertainty, i) + if uncertainty_triggered: + # uncertainty triggered + if use_recovery and not recovery_cooldown_active: + # if we have switched to recovery + if not recovery_mode: + recovery_mode = True + recovery_activated_during_rollout+=1 + recovery_steps = 0 + print(f"[RECOVERY] Step {i} - Starting recovery from state {state_guess}") + + if recovery_mode: + actions = sm_actions + recovery_steps += 1 + + # Exit recovery when task completes or after max steps + max_recovery_steps = 500 + if state_guess == 7 or recovery_steps > max_recovery_steps: # APPROACH_GOAL = 7 + print(f"[RECOVERY] Completed after {recovery_steps} steps, state={state_guess}") + recovery_mode = False + recovery_cooldown_active = True + recovery_cooldown_timer = recovery_cooldown_duration + else: + # Decrement cooldown when not in recovery + if recovery_cooldown_active: + recovery_cooldown_timer -= 1 + if recovery_cooldown_timer <= 0: + recovery_cooldown_active = False + recovery_cooldown_timer = recovery_cooldown_duration + print("Recovery Cooldown Ended") + + actions = policy_actions + + # Unnormalize actions + if args_cli.norm_factor_min is not None and args_cli.norm_factor_max is not None: + actions = ( + (actions + 1) * (args_cli.norm_factor_max - args_cli.norm_factor_min) + ) / 2 + args_cli.norm_factor_min + actions = actions.to(device=device).view(1, env.action_space.shape[1]) + + + # Apply actions + obs_dict, _, terminated, truncated, _ = env.step(actions) + recovery.append(recovery_mode) + # update obs from taking action + obs = obs_dict["policy"] + sub_obs = obs_dict["subtask_terms"] + + # Record trajectory + traj["actions"].append(actions.tolist()) + traj["next_obs"].append(obs) + traj['uncertainties'].append(uncertainty) + traj['max_actions'].append(metrics['max']) + traj['min_actions'].append(metrics['min']) + traj['time_taken'].append(metrics['time_taken']) + traj['joint_pos'].append(obs['abs_joint_pos']) + traj['recovery'].append(recovery) + if torch.any(obs['object_knocked']): + print("[MONITOR] object knocked over") + object_knocked = True + + # Check if rollout was successful + if bool(success_term.func(env, **success_term.params)[0]): + traj['failure'].append(" ") + grasp = torch.any(sub_obs['grasp']) + traj['grasp'].append(grasp.item()) + appr = torch.any(sub_obs['stacked']) + traj['appr'].append(appr.item()) + print(f"grasp : {traj['grasp']}, appr : {traj['appr']}") + # print(f"grasp : {grasp.item()}, appr : {appr.item()}") + return True, traj, recovery_activated_during_rollout, "" + + elif terminated or truncated: + grasp = torch.any(sub_obs['grasp']) + traj['grasp'].append(grasp.item()) + appr = torch.any(sub_obs['stacked']) + traj['appr'].append(appr.item()) + #print(f"grasp : {traj['grasp']}, appr : {traj['appr']}") + if i==horizon: + #if the failure was caused by object collision + traj['failure'].append("timeout") + failure_reason = "timeout" + else: + #if failure due to timeout, record timeout + traj['failure'].append("knocked") + failure_reason = "object_knocked" + return False, traj, recovery_activated_during_rollout, failure_reason + #print("REcovery : ", traj["recovery"]) + traj['failure'].append("timeout") + grasp = torch.any(sub_obs['grasp']) + traj['grasp'].append(grasp.item()) + appr = torch.any(sub_obs['stacked']) + traj['appr'].append(appr.item()) + return False, traj, recovery_activated_during_rollout, "" + + +def load_ensemble(device, ensemble_path): + """Load an ensemble of policies from checkpoint paths. + + Updated for robomimic v0.5 compatibility. + """ + with open(ensemble_path, 'r') as file: + ensemble_paths = [path.strip() for path in file.readlines()] + ensemble = [] + for path in ensemble_paths: + if not path: # Skip empty lines + continue + policy, _ = policy_from_checkpoint_v05(ckpt_path=path, device=device, verbose=True) + ensemble.append(policy) + print(f"[DEBUG] loaded {len(ensemble)} policies") + return ensemble + + +def clear_files(*paths : str): + if len(paths) < 1: + raise ValueError("At least one path is required.") + if os.path.exists(paths[0]): + #overwrite = input(f"You are about to overwrite rollout data at: {paths}\nContinue (y/n):") + overwrite = "y" + match overwrite.lower(): + case 'y' | 'Y': + for path in paths: + with open(path, 'w') as file: + pass + case _: + exit() + + + +def main(): + """Run a trained policy from robomimic with Isaac Lab environment.""" + # parse configuration + env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=1, use_fabric=not args_cli.disable_fabric) + + # Set observations to dictionary mode for Robomimic + env_cfg.observations.policy.concatenate_terms = False + + #create a log handler + # loghelper = LoggingHelper() + + + + # Disable recorder + env_cfg.recorders = None + + # Extract success checking function + success_term = env_cfg.terminations.success + env_cfg.terminations.success = None + + # Get timeout signal + timeout = env_cfg.terminations.time_out + #env_cfg.terminations.time_out = None + + # Create environment + env = gym.make(args_cli.task, cfg=env_cfg).unwrapped + + # Set seed + torch.manual_seed(args_cli.seed) + ##### unset these two lines for deterministic randomness + # np.random.seed(args_cli.seed) + # random.seed(args_cli.seed) + + env.seed(args_cli.seed) + + # Acquire device + device = TorchUtils.get_torch_device(try_to_use_cuda=True) + + # load the stack cube ensemble + # stack_cube_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/stack_cube_ensemble.txt') + + #pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/ensembles.txt') + pick_place_ensemble = load_ensemble(device, ensemble_path='docs/place/Dev-IK-Rel-Place-v0/best_models/best_model_paths.txt') + # pick_place_ensemble_30 = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/pick_place_ensemble_30_paths.txt') + + # Lets set these to the 0.99 confidence + parameters = { + 'beaker_lift' :{ + 0 : { + "confidence_level": 0.018646507043923632, + "window_size": 15, + "max_peaks": 10 + }, + 1 : { + "confidence_level": 0.018898154803458085, + "window_size": 10, + "max_peaks": 9 + }, + 2 : { + "confidence_level": 0.017589774107725654, + "window_size": 15, + "max_peaks": 7 + }, + 3 : { + "confidence_level": 0.02829341592326851, + "window_size": 12, + "max_peaks": 8 + }, + 4 : { + "confidence_level": 0.029170220902127186, + "window_size": 15, + "max_peaks": 11 + }, + 5 : { + "confidence_level": 0.017449474558650185, + "window_size": 12, + "max_peaks": 7 + }, + 6 : { + "confidence_level": 0.6324478323150455, + "window_size": 14, + "max_peaks": 14 + }, + + }, + } + + model_arch = "BC_RNN_GMM" + task = "pick_place" # stack_cube or pick_place + model_name = f"ensemble" + number = 'no_recovery_video' + + results_path = f"docs/training_data/{task}/uncertainty_rollout_{task}/{model_name}/run_{number}" + + uncertainties_path = f"{results_path}/uncertainties{number}.txt" + actions_path = f"{results_path}/actions{number}.txt" + min_actions_path = f"{results_path}/min_actions{number}.txt" + max_actions_path = f"{results_path}/max_actions{number}.txt" + time_taken_path = f"{results_path}/time_taken{number}.txt" + recovery_activated_during_rollout_path = f"{results_path}/recovery_activated_during_rollout.txt" + rollout_uncert_path = f"{uncertainties_path}/rollout_uncerts" + rollout_log_path = f"{uncertainties_path[:len(uncertainties_path)-4]}_rollout_log.txt" + + rollout_log_path = f"{uncertainties_path[:len(uncertainties_path)-4]}_rollout_log.txt" # remove the '.txt' and add rollout_log.txt + + uncertainty_results_path = f"{results_path}/uncertainty_plots" + trajectory_results_path = f"{results_path}/trajectory_plots" + os.makedirs(uncertainty_results_path, exist_ok=True) + os.makedirs(trajectory_results_path, exist_ok=True) + + + # clear files + clear_files( + uncertainties_path, actions_path, + min_actions_path, max_actions_path, + time_taken_path, recovery_activated_during_rollout_path + ) + + # Run policy + results = [] + failed_no_intervention =[] + intervention = [] + intervention_success = [] + knocked=0 + intervention_failure = [] + for trial in range(args_cli.num_rollouts): + print(f"[INFO] Starting trial {trial}") + + terminated, traj, recovery_activated_during_rollout, failure = rollout_ensemble(pick_place_ensemble[:args_cli.ensemble_size], env, success_term, args_cli.horizon, device, parameters['beaker_lift'], use_recovery=args_cli.use_recovery, rollout_num=trial) + # save the uncertainties + print("Finished rollout, recovery needed : ", recovery_activated_during_rollout) + #print("actions shape : ", traj['actions']) + + if not terminated: + print(f"failure : {traj['failure']}") + for j in range(7): + with open(f"docs/rollouts/rollout_logging_no_recovery_joint{j}.txt", "a") as file: + #file.write(f"Logs for joint {j}\n") + for i, var in enumerate(traj['actions']): + # get all the info per joint + act = str(traj['actions'][i][0][j]) + max = str(traj['max_actions'][i].values.tolist()[j]) + min = str(traj['min_actions'][i].values.tolist()[j]) + uncert = str(traj['uncertainties'][i].tolist()[j]) + #print("joint pos : ", str(traj['joint_pos'][i][0].tolist()[j])) + joint_pos = str(traj['joint_pos'][i][0].tolist()[j]) + recovery = "False"#str(traj['recovery'][0][i]) + # print("writing recovery : ", recovery) + line = f"{i} : {act} : {max} : {min} : {uncert} : {joint_pos}: {recovery}: {terminated}\n" + file.write(line) + # this logs the outcome of the rollout - the failure methods + for j in range(7): + joint_uncert = [t[j].item() for t in traj['uncertainties']] + with open(f"docs/rollouts/rollout_uncerts_joint{j}.txt", "a") as f: + line = ",".join(["{:.10f}".format(v) for v in joint_uncert]) + f.write(line + f" : {terminated} \n") + # for i in range(len(traj['failure'])): + with open(f"docs/rollouts/rollout_outcomes.txt", "a") as file: + if terminated: + # print(f"grasp : {traj['grasp']}, appr : {traj['appr']}") + #trialnum : outcome : recovery needed ? : failure mode : grasp subtask : appr subtask + line=f"{trial}: {terminated} : {recovery_activated_during_rollout} : : {True} : {True} \n" + else : + grasp = traj['grasp'][0] + appr = traj['appr'][0] + print(f"Subatask : {grasp} , {appr}") + line=f"{trial} : {terminated} : {recovery_activated_during_rollout} : {traj['failure'][0]} : {grasp} : {appr}\n" + # print(f"grasp : {traj['grasp'][0]}, appr : {traj['appr'][0]}") + file.write(line) + + + results.append(terminated) + if not terminated: + if recovery_activated_during_rollout <=0: + failed_no_intervention.append(1) + + if recovery_activated_during_rollout > 0: + intervention.append(1) + if terminated: + intervention_success.append(1) + else: + intervention_failure.append(1) + else: + intervention.append(0) + if "knocked" in traj['failure'] or (len(traj['failure']) > 0 and traj['failure'][-1] == "knocked"): + knocked+=1 + + print(f'Current success rate: {results.count(True)/len(results):.2%}') + print(f'Current intervention rate: {sum(intervention)/len(intervention):.2%} ({sum(intervention)}/{len(intervention)})') + print(f"[INFO] Trial {trial}: {'SUCCESS' if terminated else 'FAILED'}\n") + #loghelper.stopEpoch(trial) + + # Prepare summary text + exp_name = args_cli.exp_name if args_cli.exp_name else "unnamed" + summary_text = f"\nSummary for experiment {exp_name}\n" + summary_text += f"\nSuccessful trials: {results.count(True)}, out of {len(results)} trials\n" + summary_text += f"Success rate: {results.count(True) / len(results)}\n" + summary_text += f'Failed runs with no intervention : {sum(failed_no_intervention)}\n' + summary_text += f"Intervention rate : {sum(intervention)/len(intervention)}. Of which success : {sum(intervention_success)}, of which failed {sum(intervention_failure)}\n" + summary_text += f"Number of times object knocked over : {knocked}\n" + summary_text += f"Trial Results: {results}\n\n" + summary_text += f"Environment random seed: {args_cli.seed}\n" + + # Print to console + print(summary_text) + + # Write to file + results_file = "docs/ensemble_run_results.txt" + with open(results_file, 'a') as f: + f.write(summary_text) + f.write("\n" + "="*80 + "\n\n") + print(f"Results appended to: {results_file}") + + env.close() + + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/imitation_learning/robomimic/train.py b/scripts/imitation_learning/robomimic/train.py index 945c1f40f980..9203665a3ac9 100644 --- a/scripts/imitation_learning/robomimic/train.py +++ b/scripts/imitation_learning/robomimic/train.py @@ -87,7 +87,7 @@ import isaaclab_tasks.manager_based.manipulation.pick_place # noqa: F401 -def normalize_hdf5_actions(config: Config, log_dir: str) -> str: +def normalize_hdf5_actions(config: Config, log_dir: str) -> list: """Normalizes actions in hdf5 dataset to [-1, 1] range. Args: @@ -95,14 +95,22 @@ def normalize_hdf5_actions(config: Config, log_dir: str) -> str: log_dir: Directory to save normalization parameters. Returns: - Path to the normalized dataset. + List of dataset configs with normalized paths (robomimic v0.5 format). """ - base, ext = os.path.splitext(config.train.data) + # Handle both string path and list format (robomimic v0.5) + if isinstance(config.train.data, str): + original_path = config.train.data + elif isinstance(config.train.data, list): + original_path = config.train.data[0]["path"] + else: + raise ValueError(f"Unexpected config.train.data type: {type(config.train.data)}") + + base, ext = os.path.splitext(original_path) normalized_path = base + "_normalized" + ext # Copy the original dataset print(f"Creating normalized dataset at {normalized_path}") - shutil.copyfile(config.train.data, normalized_path) + shutil.copyfile(original_path, normalized_path) # Open the new dataset and normalize the actions with h5py.File(normalized_path, "r+") as f: @@ -126,11 +134,12 @@ def normalize_hdf5_actions(config: Config, log_dir: str) -> str: f[path] = normalized_data # Save the min and max values to log directory - with open(os.path.join(log_dir, "normalization_params.txt"), "w") as f: - f.write(f"min: {min}\n") - f.write(f"max: {max}\n") + with open(os.path.join(log_dir, "normalization_params.txt"), "w") as norm_f: + norm_f.write(f"min: {min}\n") + norm_f.write(f"max: {max}\n") - return normalized_path + # Return in robomimic v0.5 list format + return [{"path": normalized_path}] def train(config: Config, device: str, log_dir: str, ckpt_dir: str, video_dir: str): @@ -164,17 +173,29 @@ def train(config: Config, device: str, log_dir: str, ckpt_dir: str, video_dir: s # read config to set up metadata for observation modalities (e.g. detecting rgb observations) ObsUtils.initialize_obs_utils_with_config(config) - # make sure the dataset exists - dataset_path = os.path.expanduser(config.train.data) + # robomimic v0.5: config.train.data is a list of dataset configs + # Verify dataset exists (conversion already done in main() before lock) + dataset_path = os.path.expanduser(config.train.data[0]["path"]) if not os.path.exists(dataset_path): raise FileNotFoundError(f"Dataset at provided path {dataset_path} not found!") - - # load basic metadata from training file + + # load basic metadata from training file (use first dataset) print("\n============= Loaded Environment Metadata =============") - env_meta = FileUtils.get_env_metadata_from_dataset(dataset_path=config.train.data) + + env_meta = FileUtils.get_env_metadata_from_dataset(dataset_path=dataset_path) + # robomimic v0.5 changed API: now expects dataset_config dict with "path" key + print(f"config.train.data: {config.train.data}") + dataset_config = config.train.data[0] + + print(f"Dataset Config: {dataset_config}") + shape_meta = FileUtils.get_shape_metadata_from_dataset( - dataset_path=config.train.data, all_obs_keys=config.all_obs_keys, verbose=True + dataset_config=dataset_config, + action_keys=config.train.action_keys, + all_obs_keys=config.all_obs_keys, + verbose=True ) + print(f"shape_meta: {shape_meta}\n") if config.experiment.env is not None: env_meta["env_name"] = config.experiment.env @@ -202,9 +223,29 @@ def train(config: Config, device: str, log_dir: str, ckpt_dir: str, video_dir: s print(envs[env.name]) print("") + print(f"obs keys : {shape_meta['all_obs_keys']}") + + + # load training data BEFORE model creation (robomimic v0.5 requirement) + trainset, validset = TrainUtils.load_data_for_training(config, obs_keys=shape_meta["all_obs_keys"]) + train_sampler = trainset.get_dataset_sampler() + print("\n============= Training Dataset =============") + print(trainset) + print("") + + # robomimic v0.5: set num_train_batches and num_epochs in optim_params before model creation + train_num_steps = config.experiment.epoch_every_n_steps + for k in config.algo.optim_params: + config.algo.optim_params[k]["num_train_batches"] = len(trainset) if train_num_steps is None else train_num_steps + config.algo.optim_params[k]["num_epochs"] = config.train.num_epochs # setup for a new training run - data_logger = DataLogger(log_dir, config=config, log_tb=config.experiment.logging.log_tb) + data_logger = DataLogger( + log_dir, + config=config, + log_tb=config.experiment.logging.log_tb, + log_wandb=config.experiment.logging.log_wandb, + ) model = algo_factory( algo_name=config.algo_name, config=config, @@ -221,13 +262,6 @@ def train(config: Config, device: str, log_dir: str, ckpt_dir: str, video_dir: s print(model) # print model summary print("") - # load training data - trainset, validset = TrainUtils.load_data_for_training(config, obs_keys=shape_meta["all_obs_keys"]) - train_sampler = trainset.get_dataset_sampler() - print("\n============= Training Dataset =============") - print(trainset) - print("") - # maybe retrieve statistics for normalizing observations obs_normalization_stats = None if config.train.hdf5_normalize_obs: @@ -390,11 +424,17 @@ def main(args: argparse.Namespace): # change location of experiment directory config.train.output_dir = os.path.abspath(os.path.join("./logs", args.log_dir, args.task)) - log_dir, ckpt_dir, video_dir = TrainUtils.get_exp_dir(config) + # robomimic v0.5+ returns 4 values: log_dir, output_dir, video_dir, time_dir + log_dir, ckpt_dir, video_dir, _ = TrainUtils.get_exp_dir(config) if args.normalize_training_actions: config.train.data = normalize_hdf5_actions(config, log_dir) + # robomimic v0.5: config.train.data must be a list of dataset configs + # Convert single path string to list format if needed (must be done before lock) + if isinstance(config.train.data, str): + config.train.data = [{"path": os.path.expanduser(config.train.data)}] + # get torch device device = TorchUtils.get_torch_device(try_to_use_cuda=config.train.cuda) @@ -430,7 +470,7 @@ def main(args: argparse.Namespace): parser.add_argument("--task", type=str, default=None, help="Name of the task.") parser.add_argument("--algo", type=str, default=None, help="Name of the algorithm.") - parser.add_argument("--log_dir", type=str, default="robomimic", help="Path to log directory") + parser.add_argument("--log_dir", type=str, default="docs/place/model", help="Path to log directory") parser.add_argument("--normalize_training_actions", action="store_true", default=False, help="Normalize actions") parser.add_argument( "--epochs", diff --git a/scripts/imitation_learning/robomimic/train_ensemble.py b/scripts/imitation_learning/robomimic/train_ensemble.py index 047c62daece7..b2510eed8edb 100644 --- a/scripts/imitation_learning/robomimic/train_ensemble.py +++ b/scripts/imitation_learning/robomimic/train_ensemble.py @@ -175,10 +175,32 @@ def train(config: Config, device: str, log_dirs: list[str], ckpt_dirs: list[str] # load basic metadata from training file print("\n============= Loaded Environment Metadata =============") env_meta = FileUtils.get_env_metadata_from_dataset(dataset_path=config.train.data) + print(f"config.train.data: {config.train.data}") + with config.values_unlocked(): + config.train.data = [{'path': 'docs/place/glassware_stack_demos_split_normalized.hdf5'}] + ####### HARD CODED MAKE SURE THIS GETS UPDATED ######### + config_list= [{'path': 'docs/place/glassware_stack_demos_split_normalized.hdf5'}] + print(f"config.train.data: {config.train.data}") + # print(f"config_list: {config_list}") + # if isinstance(config.train.data, str): + # datasets = [] + # datasets.append(config.train.data) + # dataset_config = {"path": datasets} + # elif isinstance(config.train.data, dict): + # datasets = [] + # dataset_config= (config.train.data) + # dataset_config= {'path': 'docs/place/glassware_stack_demos_split_normalized.hdf5',} + # print(f"Dataset Config: {dataset_config}") + + dataset_config = config.train.data[0] shape_meta = FileUtils.get_shape_metadata_from_dataset( - dataset_path=config.train.data, all_obs_keys=config.all_obs_keys, verbose=True + dataset_config=dataset_config, + action_keys=config.train.action_keys, + all_obs_keys=config.all_obs_keys, + verbose=True ) - + print(f"shape_meta: {shape_meta}\n") + if config.experiment.env is not None: env_meta["env_name"] = config.experiment.env print("=" * 30 + "\n" + "Replacing Env to {}\n".format(env_meta["env_name"]) + "=" * 30) @@ -205,7 +227,7 @@ def train(config: Config, device: str, log_dirs: list[str], ckpt_dirs: list[str] print(envs[env.name]) print("") - + print(f"obs keys : {shape_meta['all_obs_keys']}") # load training data trainset, validset = TrainUtils.load_data_for_training(config, obs_keys=shape_meta["all_obs_keys"]) @@ -427,31 +449,22 @@ def keep_data_percentage(trainset, percentage): return trainset -def create_bootstrap_sample(trainset, seed: int): - """Create a bootstrap sample (sampling with replacement) from the training set. - - Each bootstrap sample contains N samples drawn with replacement from the original - dataset of size N. On average, ~63.2% of samples will be unique, with ~36.8% duplicates. - This creates diversity across ensemble members. +def create_bootstrap_sample(trainset, seed: int, percent: float = 0.5): + """Create a random sample (without replacement) of a percentage of the training set. Args: trainset: The original training dataset. seed: Random seed for reproducibility. - + percent: Fraction of the dataset to sample (default 0.75 for 75%). Returns: - A Subset of the training data with bootstrap sampling. + A Subset of the training data with random sampling (no replacement). """ np.random.seed(seed) total_size = len(trainset) - # Sample WITH replacement - same sample can appear multiple times - indices = np.random.choice(total_size, size=total_size, replace=True) - - # Count unique samples - unique_count = len(np.unique(indices)) - unique_percent = (unique_count / total_size) * 100 - - print(f"Bootstrap sample created: {unique_count}/{total_size} unique samples ({unique_percent:.1f}%)") - + sample_size = int(total_size * percent) + indices = np.random.choice(total_size, size=sample_size, replace=False) + indices = np.sort(indices) + print(f"Random sample created: {sample_size}/{total_size} samples ({percent*100:.1f}%)") return Subset(trainset, indices) @@ -515,7 +528,7 @@ def main(args: argparse.Namespace): new_output_dir = f"{original_output_dir}/model{i}/" config.train.output_dir = new_output_dir - log_dir, ckpt_dir, video_dir = TrainUtils.get_exp_dir(config) + log_dir, ckpt_dir, video_dir, _ = TrainUtils.get_exp_dir(config) log_dirs.append(log_dir) ckpt_dirs.append(ckpt_dir) diff --git a/scripts/tools/check_demos.py b/scripts/tools/check_demos.py new file mode 100644 index 000000000000..5ef552833b5a --- /dev/null +++ b/scripts/tools/check_demos.py @@ -0,0 +1,38 @@ +import h5py +import numpy as np + +f = h5py.File('/workspace/isaaclab/docs/place/glassware_stack_demos_split.hdf5', 'r') + +# Analyze gripper action (dim 6) over normalized time +demos = list(f['data'].keys()) +print(f"Number of demos: {len(demos)}") + +# Get a few demo lengths +lengths = [f['data'][d]['actions'].shape[0] for d in demos[:10]] +print(f"Demo lengths (first 10): {lengths}") + +# Analyze gripper at different phases +early_gripper = [] # First 10% of each demo +mid_gripper = [] # 30-50% of each demo +late_gripper = [] # Last 20% of each demo + +for demo in demos: + actions = f['data'][demo]['actions'][:] + n = len(actions) + gripper = actions[:, 6] # Gripper is dim 6 + + early_gripper.extend(gripper[:int(n*0.1)]) + mid_gripper.extend(gripper[int(n*0.3):int(n*0.5)]) + late_gripper.extend(gripper[int(n*0.8):]) + +print(f"\nGripper action analysis by phase:") +print(f"Early (0-10%): mean={np.mean(early_gripper):.3f}, std={np.std(early_gripper):.3f}") +print(f"Mid (30-50%): mean={np.mean(mid_gripper):.3f}, std={np.std(mid_gripper):.3f}") +print(f"Late (80-100%): mean={np.mean(late_gripper):.3f}, std={np.std(late_gripper):.3f}") + +# Check first few steps of first demo +first_demo = f['data'][demos[0]]['actions'][:] +print(f"\nFirst demo - first 20 gripper actions:") +print(first_demo[:20, 6]) + +f.close() \ No newline at end of file diff --git a/scripts/tools/copy_metadata_hdf5.py b/scripts/tools/copy_metadata_hdf5.py index 85fbcc84bf1b..3b57e1804b70 100644 --- a/scripts/tools/copy_metadata_hdf5.py +++ b/scripts/tools/copy_metadata_hdf5.py @@ -6,10 +6,11 @@ DATA_GROUP = "data" # Metadata to add +# robomimic v0.5 requires 'env_kwargs' key (not 'sim_args') metadata = { "env_name": "Cube-Mimic-v0", "type": 2, - "sim_args": { + "env_kwargs": { "dt": 0.01, "decimation": 2, "render_interval": 2, diff --git a/source/isaaclab/isaaclab/managers/recorder_manager.py b/source/isaaclab/isaaclab/managers/recorder_manager.py index 855c975f2a91..2f8829880807 100644 --- a/source/isaaclab/isaaclab/managers/recorder_manager.py +++ b/source/isaaclab/isaaclab/managers/recorder_manager.py @@ -429,8 +429,9 @@ def get_ep_meta(self) -> dict: """Get the episode metadata.""" if not hasattr(self._env.cfg, "get_ep_meta"): # Add basic episode metadata + # robomimic v0.5 requires 'env_kwargs' key (not 'sim_args') ep_meta = dict() - ep_meta["sim_args"] = { + ep_meta["env_kwargs"] = { "dt": self._env.cfg.sim.dt, "decimation": self._env.cfg.decimation, "render_interval": self._env.cfg.sim.render_interval, diff --git a/source/isaaclab/isaaclab/safety/switchingLogic.py b/source/isaaclab/isaaclab/safety/switchingLogic.py index bfcd265adbdf..92c188d98426 100644 --- a/source/isaaclab/isaaclab/safety/switchingLogic.py +++ b/source/isaaclab/isaaclab/safety/switchingLogic.py @@ -30,7 +30,7 @@ def halfway_check(self, step:int=0, grasp_state:bool=True): return True return False -s + def check(self, uncertainties, step) -> bool: # 1. Update windows for joint_num, unc in enumerate(uncertainties): diff --git a/source/isaaclab/isaaclab/utils/datasets/hdf5_dataset_file_handler.py b/source/isaaclab/isaaclab/utils/datasets/hdf5_dataset_file_handler.py index 13f07dddf378..882eac423d05 100644 --- a/source/isaaclab/isaaclab/utils/datasets/hdf5_dataset_file_handler.py +++ b/source/isaaclab/isaaclab/utils/datasets/hdf5_dataset_file_handler.py @@ -56,8 +56,9 @@ def create(self, file_path: str, env_name: str = None): # set environment arguments # the environment type (we use gym environment type) is set to be compatible with robomimic # Ref: https://github.com/ARISE-Initiative/robomimic/blob/master/robomimic/envs/env_base.py#L15 + # robomimic v0.5 requires 'env_kwargs' key (not 'sim_args') env_name = env_name if env_name is not None else "" - self.add_env_args({"env_name": env_name, "type": 2}) + self.add_env_args({"env_name": env_name, "type": 2, "env_kwargs": {}}) def __del__(self): """Destructor for the file handler.""" @@ -177,6 +178,16 @@ def create_dataset_helper(group, key, value): for key, value in episode.data.items(): create_dataset_helper(h5_episode_group, key, value) + # robomimic v0.5: generate next_obs by shifting obs by 1 timestep + if "obs" in episode.data and isinstance(episode.data["obs"], dict): + next_obs_group = h5_episode_group.create_group("next_obs") + for obs_key, obs_value in episode.data["obs"].items(): + if isinstance(obs_value, torch.Tensor): + obs_np = obs_value.cpu().numpy() + # next_obs[t] = obs[t+1], last timestep duplicated + next_obs_np = np.concatenate([obs_np[1:], obs_np[-1:]], axis=0) + next_obs_group.create_dataset(obs_key, data=next_obs_np, compression="gzip") + # increment total step counts self._hdf5_data_group.attrs["total"] += h5_episode_group.attrs["num_samples"] diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/__init__.py b/source/isaaclab_mimic/isaaclab_mimic/envs/__init__.py index 544d80075af4..c612ed504c9b 100644 --- a/source/isaaclab_mimic/isaaclab_mimic/envs/__init__.py +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/__init__.py @@ -19,6 +19,7 @@ from .cube_blueprint_mimic_env_cfg import CubeBlueprintMimicEnvCfg from .cube_mimic_env_cfg import CubeMimicEnvCfg from .cube_mimic_env import CubeMimicEnv +from .cube_mimic_env_skillgen_cfg import CubeMimicEnvSkillgenCfg ## # Inverse Kinematics - Relative Pose Control ## @@ -84,6 +85,15 @@ disable_env_checker=True, ) +gym.register( + id="Cube-IK-Rel-Skillgen-v0", + entry_point="isaaclab_mimic.envs:CubeMimicEnv", + kwargs={ + "env_cfg_entry_point": cube_mimic_env_skillgen_cfg.CubeMimicEnvSkillgenCfg, + }, + disable_env_checker=True, +) + gym.register( id="Isaac-Stack-Cube-Bin-Franka-IK-Rel-Mimic-v0", entry_point="isaaclab_mimic.envs:FrankaCubeStackIKRelMimicEnv", diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_blueprint_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_blueprint_mimic_env_cfg.py index 0d3e07572fd7..d85e778b4889 100755 --- a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_blueprint_mimic_env_cfg.py +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_blueprint_mimic_env_cfg.py @@ -79,62 +79,39 @@ def __post_init__(self): ) ) #lift ? - subtask_configs.append( - SubTaskConfig( - # Each subtask involves manipulation with respect to a single object frame. - object_ref="object", - # End of final subtask does not need to be detected - subtask_term_signal="lift", - # No time offsets for the final subtask - subtask_term_offset_range=(5, 10), - # Selection strategy for source subtask segment - selection_strategy="nearest_neighbor_robot_distance", - # Optional parameters for the selection strategy function - selection_strategy_kwargs={"nn_k": 3}, - # Amount of action noise to apply during this subtask - action_noise=0.03, - # Number of interpolation steps to bridge to this subtask segment - num_interpolation_steps=5, - # Additional fixed steps for the robot to reach the necessary pose - num_fixed_steps=0, - # If True, apply action noise during the interpolation phase and execution - apply_noise_during_interpolation=False, - ) - ) - subtask_configs.append( - SubTaskConfig( - # Each subtask involves manipulation with respect to a single object frame. - object_ref="object", - # End of final subtask does not need to be detected - # LOL YOU DO - subtask_term_signal="appr_goal", - # No time offsets for the final subtask - subtask_term_offset_range=(0, 0), - # Selection strategy for source subtask segment - selection_strategy="nearest_neighbor_robot_distance", - # Optional parameters for the selection strategy function - selection_strategy_kwargs={"nn_k": 3}, - # Amount of action noise to apply during this subtask - action_noise=0.03, - # Number of interpolation steps to bridge to this subtask segment - num_interpolation_steps=5, - # Additional fixed steps for the robot to reach the necessary pose - num_fixed_steps=0, - # If True, apply action noise during the interpolation phase and execution - apply_noise_during_interpolation=False, - ) - ) + # subtask_configs.append( + # SubTaskConfig( + # # Each subtask involves manipulation with respect to a single object frame. + # object_ref="object", + # # End of final subtask does not need to be detected + # subtask_term_signal="lift", + # # No time offsets for the final subtask + # subtask_term_offset_range=(5, 10), + # # Selection strategy for source subtask segment + # selection_strategy="nearest_neighbor_robot_distance", + # # Optional parameters for the selection strategy function + # selection_strategy_kwargs={"nn_k": 3}, + # # Amount of action noise to apply during this subtask + # action_noise=0.03, + # # Number of interpolation steps to bridge to this subtask segment + # num_interpolation_steps=5, + # # Additional fixed steps for the robot to reach the necessary pose + # num_fixed_steps=0, + # # If True, apply action noise during the interpolation phase and execution + # apply_noise_during_interpolation=False, + # ) + # ) # subtask_configs.append( # SubTaskConfig( # # Each subtask involves manipulation with respect to a single object frame. # object_ref="object", # # End of final subtask does not need to be detected # # LOL YOU DO - # subtask_term_signal="release_object", + # subtask_term_signal="appr_goal", # # No time offsets for the final subtask # subtask_term_offset_range=(0, 0), # # Selection strategy for source subtask segment - # selection_strategy="nearest_neighbor_object", + # selection_strategy="nearest_neighbor_robot_distance", # # Optional parameters for the selection strategy function # selection_strategy_kwargs={"nn_k": 3}, # # Amount of action noise to apply during this subtask @@ -147,4 +124,27 @@ def __post_init__(self): # apply_noise_during_interpolation=False, # ) # ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # End of final subtask does not need to be detected + # LOL YOU DO + subtask_term_signal=None, + # No time offsets for the final subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + ) + ) self.subtask_configs["franka"] = subtask_configs \ No newline at end of file diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env.py b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env.py index a295df1c7fce..195e1abf19ab 100755 --- a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env.py +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env.py @@ -113,10 +113,10 @@ def action_to_target_eef_pose(self, action: torch.Tensor) -> dict[str, torch.Ten delta_rotation_axis = delta_rotation / delta_rotation_angle # Handle invalid division for the case when delta_rotation_angle is close to zero - is_close_to_zero_angle = torch.isclose(delta_rotation_angle, torch.zeros_like(delta_rotation_angle)).squeeze(1) + is_close_to_zero_angle = torch.isclose(delta_rotation_angle, torch.zeros_like(delta_rotation_angle)).squeeze(-1) delta_rotation_axis[is_close_to_zero_angle] = torch.zeros_like(delta_rotation_axis)[is_close_to_zero_angle] - delta_quat = PoseUtils.quat_from_angle_axis(delta_rotation_angle.squeeze(1), delta_rotation_axis).squeeze(0) + delta_quat = PoseUtils.quat_from_angle_axis(delta_rotation_angle.squeeze(-1), delta_rotation_axis) delta_rot_mat = PoseUtils.matrix_from_quat(delta_quat) target_rot = torch.matmul(delta_rot_mat, curr_rot) @@ -158,8 +158,32 @@ def get_subtask_term_signals(self, env_ids: Sequence[int] | None = None) -> dict subtask_terms = self.obs_buf["subtask_terms"] #signals["appr"] = subtask_terms["appr"][env_ids] signals["grasp"] = subtask_terms["grasp"][env_ids] + signals["stacked"] = subtask_terms["stacked"][env_ids] #signals["lift"] = subtask_terms["lift"][env_ids] - signals["appr_goal"] = subtask_terms["appr_goal"][env_ids] - # signals["release_object"] = subtask_terms["release_object"][env_ids] - # final subtask is placing cubeC on cubeA (motion relative to cubeA) - but final subtask signal is not needed + #signals["appr_goal"] = subtask_terms["appr_goal"][env_ids] + #signals["release_object"] = subtask_terms["release_object"][env_ids] + # final subtask is placing cube on goal - signal needed when using --annotate_subtask_start_signals return signals + + def get_expected_attached_object(self, eef_name: str, subtask_index: int, env_cfg) -> str | None: + """ + (SkillGen) Return the expected attached object for the given EEF/subtask. + + Assumes 'stack' subtasks place the object grasped in the preceding 'grasp' subtask. + Returns None for 'grasp' (or others) at subtask start. + """ + if eef_name not in env_cfg.subtask_configs: + return None + + subtask_configs = env_cfg.subtask_configs[eef_name] + if not (0 <= subtask_index < len(subtask_configs)): + return None + + current_cfg = subtask_configs[subtask_index] + # If stacking, expect we are holding the object grasped in the prior subtask + if "stack" in str(current_cfg.subtask_term_signal).lower(): + if subtask_index > 0: + prev_cfg = subtask_configs[subtask_index - 1] + if "grasp" in str(prev_cfg.subtask_term_signal).lower(): + return prev_cfg.object_ref + return None diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py index 20181d3aa4a2..71ce2096b510 100755 --- a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py @@ -26,7 +26,7 @@ def __post_init__(self): self.datagen_config.name = "beaker_rand_scale_D0" self.datagen_config.generation_guarantee = True self.datagen_config.generation_keep_failed = False - self.datagen_config.generation_num_trials = 100 + self.datagen_config.generation_num_trials = 10 self.datagen_config.generation_select_src_per_subtask = True self.datagen_config.generation_transform_first_robot_pose = False self.datagen_config.generation_interpolate_from_last_target_pose = True @@ -81,6 +81,8 @@ def __post_init__(self): num_fixed_steps=0, # If True, apply action noise during the interpolation phase and execution apply_noise_during_interpolation=False, + description="Grasp Glassware", + next_subtask_description="Stack glassware on scale", ) ) #lift ? @@ -106,29 +108,29 @@ def __post_init__(self): # apply_noise_during_interpolation=False, # ) # ) - subtask_configs.append( - SubTaskConfig( - # Each subtask involves manipulation with respect to a single object frame. - object_ref="object", - # End of final subtask does not need to be detected - # This is actually the lift - subtask_term_signal="appr_goal", - # No time offsets for the final subtask - subtask_term_offset_range=(10, 20), - # Selection strategy for source subtask segment - selection_strategy="nearest_neighbor_robot_distance", - # Optional parameters for the selection strategy function - selection_strategy_kwargs={"nn_k": 3}, - # Amount of action noise to apply during this subtask - action_noise=0.03, - # Number of interpolation steps to bridge to this subtask segment - num_interpolation_steps=5, - # Additional fixed steps for the robot to reach the necessary pose - num_fixed_steps=0, - # If True, apply action noise during the interpolation phase and execution - apply_noise_during_interpolation=False, - ) - ) + # subtask_configs.append( + # SubTaskConfig( + # # Each subtask involves manipulation with respect to a single object frame. + # object_ref="object", + # # End of final subtask does not need to be detected + # # This is actually the lift + # subtask_term_signal="appr_goal", + # # No time offsets for the final subtask + # subtask_term_offset_range=(10, 20), + # # Selection strategy for source subtask segment + # selection_strategy="nearest_neighbor_robot_distance", + # # Optional parameters for the selection strategy function + # selection_strategy_kwargs={"nn_k": 3}, + # # Amount of action noise to apply during this subtask + # action_noise=0.03, + # # Number of interpolation steps to bridge to this subtask segment + # num_interpolation_steps=5, + # # Additional fixed steps for the robot to reach the necessary pose + # num_fixed_steps=0, + # # If True, apply action noise during the interpolation phase and execution + # apply_noise_during_interpolation=False, + # ) + # ) subtask_configs.append( SubTaskConfig( @@ -136,11 +138,11 @@ def __post_init__(self): object_ref="object", # End of final subtask - when using --annotate_subtask_start_signals, # the last subtask must also have a signal name - subtask_term_signal="success", + subtask_term_signal="stacked", # No time offsets for the final subtask subtask_term_offset_range=(0, 0), # Selection strategy for source subtask segment - selection_strategy="nearest_neighbor_robot_distance", + selection_strategy="nearest_neighbor_object_distance", # Optional parameters for the selection strategy function selection_strategy_kwargs={"nn_k": 3}, # Amount of action noise to apply during this subtask @@ -151,6 +153,7 @@ def __post_init__(self): num_fixed_steps=0, # If True, apply action noise during the interpolation phase and execution apply_noise_during_interpolation=False, + description="Stack glassware on scale", ) ) diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_skillgen_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_skillgen_cfg.py new file mode 100755 index 000000000000..196a87c6e47e --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_skillgen_cfg.py @@ -0,0 +1,90 @@ +# Copyright (c) 2024-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.cube_lift.config.franka.dev_ik_rel_skillgen import FrankaDevEnvSkillgenCfg + + +@configclass +class CubeMimicEnvSkillgenCfg(FrankaDevEnvSkillgenCfg, MimicEnvCfg): + """ + Isaac Lab Mimic environment config class for Franka Cube Stack IK Rel env. + """ + + def __post_init__(self): + # post init of parents + super().__post_init__() + # # TODO: Figure out how we can move this to the MimicEnvCfg class + # # The __post_init__() above only calls the init for FrankaCubeStackEnvCfg and not MimicEnvCfg + # # https://stackoverflow.com/questions/59986413/achieving-multiple-inheritance-using-python-dataclasses + + # Override the existing values + self.datagen_config.name = "beaker_rand_scale_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = False + self.datagen_config.generation_num_trials = 10 + self.datagen_config.generation_select_src_per_subtask = True + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.generation_relative = True + self.datagen_config.max_num_failures = 100 + self.datagen_config.seed = 101 + + # The following are the subtask configurations for the stack task. + # The following are the subtask configurations for the stack task. + subtask_configs = [] + + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="grasp", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + description="Grasp Glassware", + next_subtask_description="Stack glassware on scale", + ) + ) + + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # End of final subtask - when using --annotate_subtask_start_signals, + # the last subtask must also have a signal name + subtask_term_signal="stacked", + # No time offsets for the final subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object_distance", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=0, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + description="Stack glassware on scale", + ) + ) + self.subtask_configs["franka"] = subtask_configs \ No newline at end of file diff --git a/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/curobo_planner.py b/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/curobo_planner.py index f9c6cf69cbdb..70b7272540aa 100644 --- a/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/curobo_planner.py +++ b/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/curobo_planner.py @@ -181,7 +181,7 @@ def __init__( self.n_repeat: int | None = self.config.n_repeat self.step_size: float | None = self.config.motion_step_size self.visualize_plan: bool = self.config.visualize_plan - self.visualize_spheres: bool = self.config.visualize_spheres + self.visualize_spheres: bool = False # Log the config parameter values self.logger.info(f"Config parameter values: {self.config}") diff --git a/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/curobo_planner_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/curobo_planner_cfg.py index eaf28e0ed153..3290a42f693d 100644 --- a/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/curobo_planner_cfg.py +++ b/source/isaaclab_mimic/isaaclab_mimic/motion_planners/curobo/curobo_planner_cfg.py @@ -126,7 +126,7 @@ class CuroboPlannerCfg: time_dilation_factor: float = 1.0 """Time dilation factor for planning.""" - surface_sphere_radius: float = 0.005 + surface_sphere_radius: float = 0.001 """Radius of surface spheres for collision checking.""" # Debug and visualization @@ -136,13 +136,13 @@ class CuroboPlannerCfg: motion_step_size: float | None = None """Step size (in radians) for retiming motion plans. If None, no retiming.""" - visualize_spheres: bool = False + visualize_spheres: bool = True """Visualize robot collision spheres. Note: only works for env 0.""" visualize_plan: bool = False """Visualize motion plan in Rerun. Note: only works for env 0.""" - debug_planner: bool = False + debug_planner: bool = True """Enable detailed motion planning debug information.""" sphere_update_freq: int = 5 @@ -442,14 +442,14 @@ def franka_stack_cube_config(cls) -> "CuroboPlannerCfg": def franka_beaker_config(cls) -> "CuroboPlannerCfg": """Create configuration for Franka stacking a normal cube.""" config = cls.franka_config() - config.static_objects = ["table"] + config.static_objects = ["table", "scale"] config.visualize_plan = True config.debug_planner = True config.motion_noise_scale = 0.02 config.collision_activation_distance = 0.0 config.approach_distance = 0.05 config.retreat_distance = 0.05 - config.surface_sphere_radius = 0.005 + config.surface_sphere_radius = 0.001 config.get_world_config = lambda: config._get_world_config_with_table_adjustment() return config @@ -469,7 +469,7 @@ def from_task_name(cls, task_name: str) -> "CuroboPlannerCfg": return cls.franka_stack_cube_bin_config() elif "stack-cube" in task_lower: return cls.franka_stack_cube_config() - elif "Cube-Mimic" in task_lower: + elif "cube-ik-rel" in task_lower: return cls.franka_beaker_config() else: # Default to Franka configuration diff --git a/source/isaaclab_mimic/setup.py b/source/isaaclab_mimic/setup.py index 658aed9ee807..84f14e1f2cb1 100644 --- a/source/isaaclab_mimic/setup.py +++ b/source/isaaclab_mimic/setup.py @@ -29,7 +29,7 @@ # Check if the platform is Linux and add the dependency if platform.system() == "Linux": - EXTRAS_REQUIRE["robomimic"].append("robomimic@git+https://github.com/ARISE-Initiative/robomimic.git@v0.4.0") + EXTRAS_REQUIRE["robomimic"].append("robomimic@git+https://github.com/ARISE-Initiative/robomimic.git@v0.5.0") # Cumulation of all extra-requires EXTRAS_REQUIRE["all"] = list(itertools.chain.from_iterable(EXTRAS_REQUIRE.values())) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/diffusion_policy.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/diffusion_policy.json index 47fafeea8cb1..27353a78c1ea 100644 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/diffusion_policy.json +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/beaker_pour/config/franka/agents/robomimic/diffusion_policy.json @@ -40,6 +40,7 @@ "num_data_workers": 4, "hdf5_cache_mode": "low_dim", "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, "hdf5_normalize_obs": false, "hdf5_filter_key": "train", "hdf5_validation_filter_key": "valid", @@ -70,7 +71,10 @@ "initial": 0.0001, "decay_factor": 1.0, "epoch_schedule": [], - "scheduler_type": "constant_with_warmup" + "scheduler_type": "cosine", + "step_every_batch": true, + "warmup_steps": 500, + "num_cycles": 0.5 }, "regularization": { "L2": 0.0001 diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py index 7f7b0e7bae8d..0c07f8dcb01b 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py @@ -147,13 +147,22 @@ entry_point="isaaclab.envs:ManagerBasedRLEnv", kwargs={ "env_cfg_entry_point": f"{__name__}.dev_ik_rel_env_place:FrankaDevEnvCfg", - "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), "robomimic_bc_trans_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_trans.json"), "robomimic_hbc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/hbc.json"), "robomimic_bcq_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bcq.json"), "robomimic_diffusion_policy_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/diffusion_policy.json"), - + + }, + disable_env_checker=True, +) + +gym.register( + id="Dev-IK-Rel-Place-Skillgen-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.dev_ik_rel_skillgen:FrankaDevEnvSkillgenCfg", + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), }, disable_env_checker=True, ) \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json index d5071d660c5d..8125682666ea 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json @@ -7,7 +7,7 @@ "terminal_output_to_txt": true, "log_tb": true, "log_wandb": true, - "wandb_proj_name": "pick_place_new_demos_med_qual" + "wandb_proj_name": "pick_place_bc_rnn_0.5_mix" }, "save": { "enabled": true, @@ -20,7 +20,7 @@ }, "epoch_every_n_steps": 100, "validation_epoch_every_n_steps": 10, - "env": "Dev-IK-Rel-v1", + "env": "Dev-IK-Rel-place-v0", "additional_envs": null, "render": false, "render_video": true, @@ -28,9 +28,9 @@ "video_skip": 5, "rollout": { "enabled": false, - "terminate_on_success" : true, - "n": 50, - "horizon": 400, + "terminate_on_success": true, + "n": 50, + "horizon": 400, "rate": 50 } }, @@ -43,13 +43,15 @@ "hdf5_normalize_obs": false, "hdf5_filter_key": "train", "hdf5_validation_filter_key": "valid", - "seq_length": 100, + "seq_length": 16, "dataset_keys": [ "actions", "rewards", "dones" ], - + "action_keys": [ + "actions" + ], "goal_mode": null, "cuda": true, "batch_size": 100, @@ -76,7 +78,6 @@ "l1_weight": 0.0, "cos_weight": 0.0 }, - "gmm": { "enabled": true, "num_modes": 10, @@ -84,7 +85,6 @@ "std_activation": "softplus", "low_noise_eval": true }, - "rnn": { "enabled": true, "horizon": 100, @@ -96,7 +96,6 @@ "bidirectional": false } } - }, "observation": { "modalities": { diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/diffusion_policy.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/diffusion_policy.json index ae7cffd9ea58..9d6e437ec9b7 100644 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/diffusion_policy.json +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/diffusion_policy.json @@ -12,7 +12,7 @@ "save": { "enabled": true, "every_n_seconds": null, - "every_n_epochs": 50, + "every_n_epochs": null, "epochs": [], "on_best_validation": true, "on_best_rollout_return": false, @@ -43,11 +43,10 @@ "hdf5_normalize_obs": false, "hdf5_filter_key": "train", "hdf5_validation_filter_key": "valid", - "seq_length": 10, - "frame_stack": 1, - "pad_frame_stack": true, + "seq_length": 16, "pad_seq_length": true, - "get_pad_mask": false, + "frame_stack": 2, + "pad_frame_stack": true, "goal_mode": null, "dataset_keys": [ "actions", @@ -58,8 +57,8 @@ "actions" ], "cuda": true, - "batch_size": 256, - "num_epochs": 3000, + "batch_size": 128, + "num_epochs": 1000, "seed": 101 }, "algo": { @@ -68,9 +67,12 @@ "optimizer_type": "adamw", "learning_rate": { "initial": 0.0001, - "decay_factor": 1.0, + "decay_factor": 0.1, "epoch_schedule": [], - "scheduler_type": "constant_with_warmup" + "scheduler_type": "cosine", + "step_every_batch": true, + "warmup_steps": 500, + "num_cycles": 0.5 }, "regularization": { "L2": 0.0001 @@ -79,7 +81,7 @@ }, "horizon": { "observation_horizon": 2, - "action_horizon": 8, + "action_horizon": 4, "prediction_horizon": 16 }, "unet": { diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_env_cfg.py index 896391c9b6dd..2c2eba3b3b98 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_env_cfg.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_env_cfg.py @@ -67,6 +67,9 @@ def __post_init__(self): open_command_expr={"panda_finger_.*": 0.04}, close_command_expr={"panda_finger_.*": 0.0}, ) + self.gripper_joint_names = ["panda_finger_.*"] + self.gripper_open_val = 0.04 + self.gripper_threshold = 0.005 # Set the body name for the end effector self.commands.object_pose.body_name = "panda_hand" self.commands.object_pose.body_name = "panda_hand" diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py index fd754ccfbc58..223211fd0669 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py @@ -76,10 +76,10 @@ def __post_init__(self): scale=0.5, body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), ) - self.terminations.success=DoneTerm(func=mdp.object_stacked_upright, params={"lower_object_cfg": SceneEntityCfg("scale")}) + #self.terminations.success=DoneTerm(func=mdp.object_stacked_upright, params={"lower_object_cfg": SceneEntityCfg("scale")}) - self.observations.subtask_terms.appr_goal=ObsTerm(func=mdp.is_object_lifted, params={"threshold":0.15} - ) + #self.observations.subtask_terms.appr_goal=ObsTerm(func=mdp.is_object_lifted, params={"threshold":0.15} + #) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_skillgen.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_skillgen.py new file mode 100755 index 000000000000..0872b185059a --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_skillgen.py @@ -0,0 +1,87 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass +from isaaclab.assets import RigidObjectCfg, ArticulationCfg +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.assets import AssetBaseCfg +from isaaclab.sim.spawners.from_files import UsdFileCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from source.isaaclab_assets.isaaclab_assets.robots.universal_robots import UR10_CFG +from . import dev_env_cfg +import math +from isaaclab.managers import SceneEntityCfg +from isaaclab_assets.glassware.glassware import ChemistryGlassware +from isaaclab_tasks.manager_based.manipulation.cube_lift import mdp +from isaaclab.managers import TerminationTermCfg as DoneTerm +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip +from isaaclab_assets.robots.universal_robots import UR10e_ROBOTIQ_GRIPPER_CFG + + + +@configclass +class FrankaDevEnvSkillgenCfg(dev_env_cfg.FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # put the beaker on the stir plate + glassware = ChemistryGlassware() + self.scene.stirplate = glassware.stirplate(pos=[0.5, 0.0, 0.01]) + self.scene.scale = glassware.scale(pos=[0.3, -0.3, 0.01]) + + self.events.reset_object_position = EventTerm( + func=mdp.reset_place_root_state_uniform, + mode="reset", + params={ + "pose_range": {"x": (0, 0.2), "y": (0, 0.25), "z": (0.02, 0.02)}, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("object", body_names="Object"), + "asset2_cfg" : SceneEntityCfg("stirplate"), + "asset3_cfg" : SceneEntityCfg("scale") + + }, + ) + #self.scene.robot = UR10e_ROBOTIQ_GRIPPER_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace( + prim_path="{ENV_REGEX_NS}/Robot", + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + + "panda_joint1": 0.3281, + "panda_joint2": -0.3684, + "panda_joint3": -0.2787, + "panda_joint4": -2.6138, + "panda_joint5": -2.7527, + "panda_joint6": 2.4991, # +90° → keeps hand level + "panda_joint7": 0.3331, + "panda_finger_joint1": 0.04, # open gripper + "panda_finger_joint2": 0.04, + } + ), + ) + # replace with relative position controller + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + ) + #self.terminations.success=DoneTerm(func=mdp.object_stacked_upright, params={"lower_object_cfg": SceneEntityCfg("scale")}) + + #self.observations.subtask_terms.appr_goal=ObsTerm(func=mdp.is_object_lifted, params={"threshold":0.15} + #) + # for f in self.scene.ee_frame.target_frames: + # if f.name == "end_effector": + # f.offset.pos = [0.0, 0.0, 0.0] + # break + diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_vial_insert.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_vial_insert.py new file mode 100644 index 000000000000..40a0566b68d5 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_vial_insert.py @@ -0,0 +1,97 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass +from isaaclab.assets import RigidObjectCfg, ArticulationCfg +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.assets import AssetBaseCfg +from isaaclab.sim.spawners.from_files import UsdFileCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from source.isaaclab_assets.isaaclab_assets.robots.universal_robots import UR10_CFG +from . import dev_env_cfg +import math +from isaaclab.managers import SceneEntityCfg +from isaaclab_assets.glassware.glassware import ChemistryGlassware +from isaaclab_tasks.manager_based.manipulation.cube_lift import mdp +from isaaclab.managers import TerminationTermCfg as DoneTerm +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip +from isaaclab_assets.robots.universal_robots import UR10e_ROBOTIQ_GRIPPER_CFG + + + +@configclass +class FrankaDevEnvCfg(dev_env_cfg.FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # get the vial from the stirplate and place in vial rack + glassware = ChemistryGlassware() + self.scene.object=glassware.vial(pos=[0.5, 0.0, 0.02],name="Object") + self.scene.stirplate = glassware.stirplate(pos=[0.5, 0.0, 0.01]) + + self.scene.vialrack = glassware.vialrack(pos=[0.3, -0.3, 0.01]) + + self.events.reset_object_position = EventTerm( + func=mdp.reset_place_root_state_uniform, + mode="reset", + params={ + "pose_range": {"x": (0, 0.2), "y": (0, 0.25), "z": (0.02, 0.02)}, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("object", body_names="Object"), + "asset2_cfg" : SceneEntityCfg("stirplate"), + "asset3_cfg" : SceneEntityCfg("scale") + + }, + ) + #self.scene.robot = UR10e_ROBOTIQ_GRIPPER_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace( + prim_path="{ENV_REGEX_NS}/Robot", + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + + "panda_joint1": 0.3281, + "panda_joint2": -0.3684, + "panda_joint3": -0.2787, + "panda_joint4": -2.6138, + "panda_joint5": -2.7527, + "panda_joint6": 2.4991, # +90° → keeps hand level + "panda_joint7": 0.3331, + "panda_finger_joint1": 0.04, # open gripper + "panda_finger_joint2": 0.04, + } + ), + ) + # replace with relative position controller + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + ) + self.terminations.success=DoneTerm(func=mdp.object_inserted_upright, params={"lower_object_cfg": SceneEntityCfg("vialrack")}) + + #self.observations.subtask_terms.appr_goal=ObsTerm(func=mdp.is_object_lifted, params={"threshold":0.15} + #) + + + +@configclass +class FrankaCubeEnvCfg_PLAY(FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py index eb2138fa5ddf..9b74ea1e1364 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py @@ -182,6 +182,14 @@ class SubtaskCfg(ObsGroup): "object_cfg": SceneEntityCfg("object"), }, ) + stacked = ObsTerm( + func=mdp.object_stacked, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "upper_object_cfg": SceneEntityCfg("object"), + "lower_object_cfg": SceneEntityCfg("scale"), + }, + ) # lift = ObsTerm( # func=mdp.is_object_lifted, # params={ @@ -189,13 +197,13 @@ class SubtaskCfg(ObsGroup): # "threshold" : 0.1 # } # ) - appr_goal = ObsTerm( - func=mdp.object_near_goal, - params={ - "threshold": 0.05, - "command_name": "object_pose", - }, - ) + # appr_goal = ObsTerm( + # func=mdp.object_near_goal, + # params={ + # "threshold": 0.05, + # "command_name": "object_pose", + # }, + # ) # release_object = ObsTerm( # func=mdp.object_released, # params={ @@ -308,7 +316,8 @@ class TerminationsCfg: ) object_tipped = DoneTerm(func=mdp.object_knocked) #fix object dropping - success = DoneTerm(func=mdp.object_near_goal) + #success = DoneTerm(func=mdp.object_near_goal) + success= DoneTerm(func=mdp.object_stacked_upright, params={"lower_object_cfg": SceneEntityCfg("scale")}) @configclass diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py index 75355c70d87a..119f5ef00407 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py @@ -151,7 +151,7 @@ def is_object_lifted( ): #return true when object z coord above a threshold value object = env.scene[obj_cfg.name] - print(f"Object z pos : {object.data.root_pos_w[:, 2]}") + # print(f"Object z pos : {object.data.root_pos_w[:, 2]}") # if object.data.root_pos_w[:, 2].item() > threshold : # print(f"Observed Object Lifted : {object.data.root_pos_w[:, 2].item()}") # loghelper.logsubtask(LogType.LIFT) @@ -304,10 +304,8 @@ def object_tilt (env: ManagerBasedRLEnv, upper_object_cfg: SceneEntityCfg = Scen def object_knocked(env: ManagerBasedRLEnv, upper_object_cfg: SceneEntityCfg = SceneEntityCfg("object"), max_tilt: float = 45): object: RigidObject = env.scene[upper_object_cfg.name] tilt_deg = upright_tilt_deg(object.data.root_quat_w, object.data.default_root_state[:, 3:7]) - if tilt_deg.cpu().detach().numpy()[0] > max_tilt: - # print("object knocked") - return torch.tensor([True], device='cuda:0') - else :return torch.tensor([False], device='cuda:0') + # Return a bool tensor with shape (num_envs,) for proper batching + return tilt_deg > max_tilt def target_position( env: ManagerBasedRLEnv, @@ -318,4 +316,28 @@ def target_position( object: RigidObject = env.scene[object_cfg.name] object_pos_w = object.data.root_pos_w[:, :3] #print(f"target position : {object_pos_w}") - return object_pos_w \ No newline at end of file + return object_pos_w + + +def target_pose( + env: ManagerBasedRLEnv, + object_cfg: SceneEntityCfg = SceneEntityCfg("scale"), +) -> torch.Tensor: + """The pose (position + quaternion) of the target object in world frame. + + Returns: + Tensor of shape (num_envs, 7) containing [x, y, z, qw, qx, qy, qz]. + """ + object: RigidObject = env.scene[object_cfg.name] + object_pos_w = object.data.root_pos_w[:, :3] # (num_envs, 3) + object_quat_w = object.data.root_quat_w # (num_envs, 4) - already wxyz format + return torch.cat([object_pos_w, object_quat_w], dim=-1) # (num_envs, 7) + +# def object_distance_to_goal( +# env: ManagerBasedRLEnv, +# object_cfg: SceneEntityCfg = SceneEntityCfg("object"), +# goal_object_location: SceneEntityCfg = SceneEntityCfg("vialrack"), +# ) -> torch.Tensor: + + + diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/terminations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/terminations.py index 6065810915a6..fa2ab92788f9 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/terminations.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/terminations.py @@ -99,16 +99,39 @@ def placed( def object_stacked_upright(env: ManagerBasedRLEnv, robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), upper_object_cfg: SceneEntityCfg = SceneEntityCfg("object"),lower_object_cfg: SceneEntityCfg = SceneEntityCfg("goal"), - xy_threshold: float = 0.1, height_threshold: float = 0.1, height_diff: float = 0.05, + xy_threshold: float = 0.05, height_threshold: float = 0.1, height_diff: float = 0.05, + atol=0.0001, + rtol=0.0001, upright_good_deg: float = 30.0, gripper_open_val: torch.Tensor = torch.tensor([0.04]), logging=False) -> torch.Tensor: - """Stacked AND sufficiently upright (≤ upright_good_deg).""" + + """Stacked AND sufficiently upright (≤ upright_good_deg).""" + robot: Articulation = env.scene[robot_cfg.name] upper: RigidObject = env.scene[upper_object_cfg.name] tilt_deg = upright_tilt_deg(upper.data.root_quat_w, upper.data.default_root_state[:, 3:7]) upright_good = tilt_deg <= upright_good_deg stacked = object_stacked(env, robot_cfg, upper_object_cfg, lower_object_cfg, xy_threshold, height_threshold, height_diff, gripper_open_val) stacked_upright = stacked & upright_good + gripper_joint_ids, _ = robot.find_joints(env.cfg.gripper_joint_names) + stacked = torch.logical_and( + torch.isclose( + robot.data.joint_pos[:, gripper_joint_ids[0]], + torch.tensor(env.cfg.gripper_open_val, dtype=torch.float32).to(env.device), + atol=atol, + rtol=rtol, + ), + stacked_upright, + ) + stacked = torch.logical_and( + torch.isclose( + robot.data.joint_pos[:, gripper_joint_ids[1]], + torch.tensor(env.cfg.gripper_open_val, dtype=torch.float32).to(env.device), + atol=atol, + rtol=rtol, + ), + stacked, + ) #if logging: #rising = log(env, "object_stacked_upright", stacked_upright, f"Stacked (upright ≤ {upright_good_deg}°)") @@ -116,7 +139,7 @@ def object_stacked_upright(env: ManagerBasedRLEnv, robot_cfg: SceneEntityCfg = S #if idx.numel() > 0: # print(f"[stacked-upright tilt] n={int(idx.numel())} sample={tilt_deg[idx][:5].tolist()}") - return stacked_upright + return stacked def upright_tilt_deg(q_cur, q_init) -> torch.Tensor: @@ -127,7 +150,7 @@ def upright_tilt_deg(q_cur, q_init) -> torch.Tensor: def object_stacked(env: ManagerBasedRLEnv, robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), upper_object_cfg: SceneEntityCfg = SceneEntityCfg("object"), lower_object_cfg: SceneEntityCfg = SceneEntityCfg("goal"), - xy_threshold: float = 0.1, height_threshold: float = 0.1, height_diff: float = 0.05, + xy_threshold: float = 0.05, height_threshold: float = 0.1, height_diff: float = 0.05, gripper_open_val: torch.Tensor = torch.tensor([0.04]),logging=False) -> torch.Tensor: """Check if an object is stacked by the specified robot.""" @@ -142,5 +165,49 @@ def object_stacked(env: ManagerBasedRLEnv, robot_cfg: SceneEntityCfg = SceneEnti # gripper_open_val.to(env.device), atol=1e-4, rtol=1e-4), stacked) # stacked = torch.logical_and(torch.isclose(robot.data.joint_pos[:, -2], # gripper_open_val.to(env.device), atol=1e-4, rtol=1e-4), stacked) - print(f"For DEBUG : STACKED STATUS : {stacked}") - return stacked \ No newline at end of file + #print(f"For DEBUG : STACKED STATUS : {stacked}") + return stacked + +def object_inserted_upright(env: ManagerBasedRLEnv, robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), + upper_object_cfg: SceneEntityCfg = SceneEntityCfg("object"),lower_object_cfg: SceneEntityCfg = SceneEntityCfg("vialrack"), + xy_threshold: float = 0.01, height_threshold: float = 0.01, height_diff: float = 0.05, + atol=0.0001, + rtol=0.0001, + upright_good_deg: float = 30.0, gripper_open_val: torch.Tensor = torch.tensor([0.04]), logging=False) -> torch.Tensor: + + + """Stacked AND sufficiently upright (≤ upright_good_deg).""" + robot: Articulation = env.scene[robot_cfg.name] + upper: RigidObject = env.scene[upper_object_cfg.name] + tilt_deg = upright_tilt_deg(upper.data.root_quat_w, upper.data.default_root_state[:, 3:7]) + upright_good = tilt_deg <= upright_good_deg + stacked = object_stacked(env, robot_cfg, upper_object_cfg, lower_object_cfg, + xy_threshold, height_threshold, height_diff, gripper_open_val) + stacked_upright = stacked & upright_good + gripper_joint_ids, _ = robot.find_joints(env.cfg.gripper_joint_names) + stacked = torch.logical_and( + torch.isclose( + robot.data.joint_pos[:, gripper_joint_ids[0]], + torch.tensor(env.cfg.gripper_open_val, dtype=torch.float32).to(env.device), + atol=atol, + rtol=rtol, + ), + stacked_upright, + ) + stacked = torch.logical_and( + torch.isclose( + robot.data.joint_pos[:, gripper_joint_ids[1]], + torch.tensor(env.cfg.gripper_open_val, dtype=torch.float32).to(env.device), + atol=atol, + rtol=rtol, + ), + stacked, + ) + + #if logging: + #rising = log(env, "object_stacked_upright", stacked_upright, f"Stacked (upright ≤ {upright_good_deg}°)") + # idx = torch.nonzero(rising, as_tuple=False).squeeze(-1) + #if idx.numel() > 0: + # print(f"[stacked-upright tilt] n={int(idx.numel())} sample={tilt_deg[idx][:5].tolist()}") + + return inserted \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_env_cfg.py index f173ee644cef..ad944c137153 100644 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_env_cfg.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_env_cfg.py @@ -40,30 +40,3 @@ def __post_init__(self): body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), ) - self.teleop_devices = DevicesCfg( - devices={ - "handtracking": OpenXRDeviceCfg( - retargeters=[ - Se3RelRetargeterCfg( - bound_hand=OpenXRDevice.TrackingTarget.HAND_RIGHT, - zero_out_xy_rotation=True, - use_wrist_rotation=False, - use_wrist_position=True, - delta_pos_scale_factor=10.0, - delta_rot_scale_factor=10.0, - sim_device=self.sim.device, - ), - GripperRetargeterCfg( - bound_hand=OpenXRDevice.TrackingTarget.HAND_RIGHT, sim_device=self.sim.device - ), - ], - sim_device=self.sim.device, - xr_cfg=self.xr, - ), - "keyboard": Se3KeyboardCfg( - pos_sensitivity=0.05, - rot_sensitivity=0.05, - sim_device=self.sim.device, - ), - } - ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_instance_randomize_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_instance_randomize_env_cfg.py index cee2530ee4f7..d647f76f3d94 100644 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_instance_randomize_env_cfg.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_ik_rel_instance_randomize_env_cfg.py @@ -6,7 +6,7 @@ from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg from isaaclab.utils import configclass - +from isaaclab_assets.glassware.glassware import ChemistryGlassware from . import stack_joint_pos_instance_randomize_env_cfg ## @@ -28,7 +28,7 @@ def __post_init__(self): self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") # Reduce the number of environments due to camera resources - self.scene.num_envs = 2 + self.scene.num_envs = 1 # Set actions for the specific robot type (franka) self.actions.arm_action = DifferentialInverseKinematicsActionCfg( diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_joint_pos_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_joint_pos_env_cfg.py index 6b8561684fe0..a73d35ee5f61 100644 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_joint_pos_env_cfg.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_joint_pos_env_cfg.py @@ -21,8 +21,8 @@ # Pre-defined configs ## from isaaclab.markers.config import FRAME_MARKER_CFG # isort: skip -from isaaclab_assets.robots.franka import FRANKA_PANDA_CFG # isort: skip - +from isaaclab_assets.robots.franka import FRANKA_PANDA_CFG +from isaaclab_assets.glassware.glassware import ChemistryGlassware @configclass class EventCfg: @@ -32,7 +32,8 @@ class EventCfg: func=franka_stack_events.set_default_joint_pose, mode="reset", params={ - "default_pose": [0.0444, -0.1894, -0.1107, -2.5148, 0.0044, 2.3775, 0.6952, 0.0400, 0.0400], + "default_pose": [0.3281, -0.3684, -0.2787, -2.6138, -2.7527, 2.4999, 0.3331, 0.0400, 0.0400], + #"default_pose": [0.0444, -0.1894, -0.1107, -2.5148, 0.0044, 2.3775, 0.6952, 0.0400, 0.0400], }, ) @@ -50,8 +51,8 @@ class EventCfg: func=franka_stack_events.randomize_object_pose, mode="reset", params={ - "pose_range": {"x": (0.4, 0.6), "y": (-0.10, 0.10), "z": (0.0203, 0.0203), "yaw": (-1.0, 1, 0)}, - "min_separation": 0.1, + "pose_range": {"x": (0.4, 0.6), "y": (-0.10, 0.10), "z": (0.0203, 0.0203)}, + "min_separation": 0.4, "asset_cfgs": [SceneEntityCfg("cube_1"), SceneEntityCfg("cube_2"), SceneEntityCfg("cube_3")], }, ) @@ -101,38 +102,11 @@ def __post_init__(self): max_depenetration_velocity=5.0, disable_gravity=False, ) - + glassware = ChemistryGlassware() # Set each stacking cube deterministically - self.scene.cube_1 = RigidObjectCfg( - prim_path="{ENV_REGEX_NS}/Cube_1", - init_state=RigidObjectCfg.InitialStateCfg(pos=[0.4, 0.0, 0.0203], rot=[1, 0, 0, 0]), - spawn=UsdFileCfg( - usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/blue_block.usd", - scale=(1.0, 1.0, 1.0), - rigid_props=cube_properties, - semantic_tags=[("class", "cube_1")], - ), - ) - self.scene.cube_2 = RigidObjectCfg( - prim_path="{ENV_REGEX_NS}/Cube_2", - init_state=RigidObjectCfg.InitialStateCfg(pos=[0.55, 0.05, 0.0203], rot=[1, 0, 0, 0]), - spawn=UsdFileCfg( - usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/red_block.usd", - scale=(1.0, 1.0, 1.0), - rigid_props=cube_properties, - semantic_tags=[("class", "cube_2")], - ), - ) - self.scene.cube_3 = RigidObjectCfg( - prim_path="{ENV_REGEX_NS}/Cube_3", - init_state=RigidObjectCfg.InitialStateCfg(pos=[0.60, -0.1, 0.0203], rot=[1, 0, 0, 0]), - spawn=UsdFileCfg( - usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/green_block.usd", - scale=(1.0, 1.0, 1.0), - rigid_props=cube_properties, - semantic_tags=[("class", "cube_3")], - ), - ) + self.scene.cube_1 = glassware.scale(pos=[0.4, -0.35, 0.01], name="cube_1"), + self.scene.cube_2 = glassware.beaker(pos=[0.45, 0.25, 0.02], name="cube_2"), + # Listens to the required transforms marker_cfg = FRAME_MARKER_CFG.copy() diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_joint_pos_instance_randomize_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_joint_pos_instance_randomize_env_cfg.py index 360f1f6482e1..dc105e2d59bf 100644 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_joint_pos_instance_randomize_env_cfg.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/config/franka/stack_joint_pos_instance_randomize_env_cfg.py @@ -5,15 +5,12 @@ import torch -from isaaclab.assets import RigidObjectCfg, RigidObjectCollectionCfg +from isaaclab.assets import RigidObjectCollectionCfg from isaaclab.managers import EventTermCfg as EventTerm from isaaclab.managers import SceneEntityCfg from isaaclab.sensors import FrameTransformerCfg from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg -from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg -from isaaclab.sim.spawners.from_files.from_files_cfg import UsdFileCfg from isaaclab.utils import configclass -from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR from isaaclab_tasks.manager_based.manipulation.stack_glassware import mdp from isaaclab_tasks.manager_based.manipulation.stack_glassware.mdp import franka_stack_events @@ -21,6 +18,8 @@ StackInstanceRandomizeEnvCfg, ) +from isaaclab_assets.glassware.glassware import ChemistryGlassware + ## # Pre-defined configs ## @@ -36,7 +35,7 @@ class EventCfg: func=franka_stack_events.set_default_joint_pose, mode="startup", params={ - "default_pose": [0.0444, -0.1894, -0.1107, -2.5148, 0.0044, 2.3775, 0.6952, 0.0400, 0.0400], + "default_pose": [0.3281, -0.3684, -0.2787, -2.6138, -2.7527, 2.4999, 0.3331, 0.0400, 0.0400], }, ) @@ -54,10 +53,10 @@ class EventCfg: func=franka_stack_events.randomize_rigid_objects_in_focus, mode="reset", params={ - "asset_cfgs": [SceneEntityCfg("cube_1"), SceneEntityCfg("cube_2"), SceneEntityCfg("cube_3")], + "asset_cfgs": [SceneEntityCfg("cube_1"), SceneEntityCfg("cube_2")], "out_focus_state": torch.tensor([10.0, 10.0, 10.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), - "pose_range": {"x": (0.4, 0.6), "y": (-0.10, 0.10), "z": (0.0203, 0.0203), "yaw": (-1.0, 1, 0)}, - "min_separation": 0.1, + "pose_range": {"x": (0.3, 0.65), "y": (-0.35, 0.30), "z": (0.02, 0.02)}, + "min_separation": 0.15, }, ) @@ -75,7 +74,7 @@ def __post_init__(self): self.scene.robot = FRANKA_PANDA_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") # Reduce the number of environments due to camera resources - self.scene.num_envs = 2 + self.scene.num_envs = 1 # Set actions for the specific robot type (franka) self.actions.arm_action = mdp.JointPositionActionCfg( @@ -92,83 +91,23 @@ def __post_init__(self): self.gripper_open_val = 0.04 self.gripper_threshold = 0.005 - # Rigid body properties of each cube - cube_properties = RigidBodyPropertiesCfg( - solver_position_iteration_count=16, - solver_velocity_iteration_count=1, - max_angular_velocity=1000.0, - max_linear_velocity=1000.0, - max_depenetration_velocity=5.0, - disable_gravity=False, - ) + # Initialize ChemistryGlassware for creating glassware objects + glassware = ChemistryGlassware() - # Set each stacking cube to be a collection of rigid objects + # cube_1: Scale (target placement surface) - two variants at different positions cube_1_config_dict = { - "blue_cube": RigidObjectCfg( - prim_path="{ENV_REGEX_NS}/Cube_1_Blue", - init_state=RigidObjectCfg.InitialStateCfg(pos=[0.4, 0.0, 0.0203], rot=[1, 0, 0, 0]), - spawn=UsdFileCfg( - usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/blue_block.usd", - scale=(1.0, 1.0, 1.0), - rigid_props=cube_properties, - ), - ), - "red_cube": RigidObjectCfg( - prim_path="{ENV_REGEX_NS}/Cube_1_Red", - init_state=RigidObjectCfg.InitialStateCfg(pos=[0.4, 0.0, 0.0403], rot=[1, 0, 0, 0]), - spawn=UsdFileCfg( - usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/red_block.usd", - scale=(1.0, 1.0, 1.0), - rigid_props=cube_properties, - ), - ), + "scale_1": glassware.scale(pos=[0.4, -0.35, 0.01], name="Scale_1"), + "scale_2": glassware.scale(pos=[10.0, 10.0, 0.01], name="Scale_2"), # Out of focus position } + # cube_2: Beaker and Conical Flask - positioned far right (positive y) cube_2_config_dict = { - "red_cube": RigidObjectCfg( - prim_path="{ENV_REGEX_NS}/Cube_2_Red", - init_state=RigidObjectCfg.InitialStateCfg(pos=[0.55, 0.05, 0.0203], rot=[1, 0, 0, 0]), - spawn=UsdFileCfg( - usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/red_block.usd", - scale=(1.0, 1.0, 1.0), - rigid_props=cube_properties, - ), - ), - "yellow_cube": RigidObjectCfg( - prim_path="{ENV_REGEX_NS}/Cube_2_Yellow", - init_state=RigidObjectCfg.InitialStateCfg(pos=[0.55, 0.05, 0.0403], rot=[1, 0, 0, 0]), - spawn=UsdFileCfg( - usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/yellow_block.usd", - scale=(1.0, 1.0, 1.0), - rigid_props=cube_properties, - ), - ), - } - - cube_3_config_dict = { - "yellow_cube": RigidObjectCfg( - prim_path="{ENV_REGEX_NS}/Cube_3_Yellow", - init_state=RigidObjectCfg.InitialStateCfg(pos=[0.60, -0.1, 0.0203], rot=[1, 0, 0, 0]), - spawn=UsdFileCfg( - usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/yellow_block.usd", - scale=(1.0, 1.0, 1.0), - rigid_props=cube_properties, - ), - ), - "green_cube": RigidObjectCfg( - prim_path="{ENV_REGEX_NS}/Cube_2_Green", - init_state=RigidObjectCfg.InitialStateCfg(pos=[0.60, -0.1, 0.0403], rot=[1, 0, 0, 0]), - spawn=UsdFileCfg( - usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/green_block.usd", - scale=(1.0, 1.0, 1.0), - rigid_props=cube_properties, - ), - ), + "beaker": glassware.beaker(pos=[0.45, 0.25, 0.02], name="Beaker_1"), + "flask": glassware.flask(pos=[10.0, 10.0, 0.02], name="Flask_1", scale=(0.7, 0.7, 0.7)), # Out of focus position } self.scene.cube_1 = RigidObjectCollectionCfg(rigid_objects=cube_1_config_dict) self.scene.cube_2 = RigidObjectCollectionCfg(rigid_objects=cube_2_config_dict) - self.scene.cube_3 = RigidObjectCollectionCfg(rigid_objects=cube_3_config_dict) # Listens to the required transforms marker_cfg = FRAME_MARKER_CFG.copy() diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/franka_stack_events.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/franka_stack_events.py index 009a44b1b372..5dc97b3e05b2 100644 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/franka_stack_events.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/franka_stack_events.py @@ -231,9 +231,13 @@ def randomize_rigid_objects_in_focus( object_states = torch.stack([out_focus_state] * asset.num_objects).to(device=env.device) pose_tensor = torch.tensor([pose_list[asset_idx]], device=env.device) positions = pose_tensor[:, 0:3] + env.scene.env_origins[cur_env, 0:3] - orientations = math_utils.quat_from_euler_xyz(pose_tensor[:, 3], pose_tensor[:, 4], pose_tensor[:, 5]) + + # Keep the current/default orientation instead of randomizing + # default_object_state shape is [num_envs, num_objects, 13] - get orientation (indices 3:7) + current_orientation = asset.data.default_object_state[cur_env, object_id, 3:7] + object_states[object_id, 0:3] = positions - object_states[object_id, 3:7] = orientations + object_states[object_id, 3:7] = current_orientation asset.write_object_state_to_sim( object_state=object_states, env_ids=torch.tensor([cur_env], device=env.device) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/observations.py index 2f65cd916ee9..d1e127d4a4c6 100644 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/observations.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/observations.py @@ -21,91 +21,76 @@ def cube_positions_in_world_frame( env: ManagerBasedRLEnv, cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), - cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), ) -> torch.Tensor: """The position of the cubes in the world frame.""" cube_1: RigidObject = env.scene[cube_1_cfg.name] cube_2: RigidObject = env.scene[cube_2_cfg.name] - cube_3: RigidObject = env.scene[cube_3_cfg.name] - return torch.cat((cube_1.data.root_pos_w, cube_2.data.root_pos_w, cube_3.data.root_pos_w), dim=1) + return torch.cat((cube_1.data.root_pos_w, cube_2.data.root_pos_w), dim=1) def instance_randomize_cube_positions_in_world_frame( env: ManagerBasedRLEnv, cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), - cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), ) -> torch.Tensor: """The position of the cubes in the world frame.""" if not hasattr(env, "rigid_objects_in_focus"): - return torch.full((env.num_envs, 9), fill_value=-1) + return torch.full((env.num_envs, 6), fill_value=-1) cube_1: RigidObjectCollection = env.scene[cube_1_cfg.name] cube_2: RigidObjectCollection = env.scene[cube_2_cfg.name] - cube_3: RigidObjectCollection = env.scene[cube_3_cfg.name] cube_1_pos_w = [] cube_2_pos_w = [] - cube_3_pos_w = [] for env_id in range(env.num_envs): cube_1_pos_w.append(cube_1.data.object_pos_w[env_id, env.rigid_objects_in_focus[env_id][0], :3]) cube_2_pos_w.append(cube_2.data.object_pos_w[env_id, env.rigid_objects_in_focus[env_id][1], :3]) - cube_3_pos_w.append(cube_3.data.object_pos_w[env_id, env.rigid_objects_in_focus[env_id][2], :3]) cube_1_pos_w = torch.stack(cube_1_pos_w) cube_2_pos_w = torch.stack(cube_2_pos_w) - cube_3_pos_w = torch.stack(cube_3_pos_w) - return torch.cat((cube_1_pos_w, cube_2_pos_w, cube_3_pos_w), dim=1) + return torch.cat((cube_1_pos_w, cube_2_pos_w), dim=1) def cube_orientations_in_world_frame( env: ManagerBasedRLEnv, cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), - cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), ): """The orientation of the cubes in the world frame.""" cube_1: RigidObject = env.scene[cube_1_cfg.name] cube_2: RigidObject = env.scene[cube_2_cfg.name] - cube_3: RigidObject = env.scene[cube_3_cfg.name] - return torch.cat((cube_1.data.root_quat_w, cube_2.data.root_quat_w, cube_3.data.root_quat_w), dim=1) + return torch.cat((cube_1.data.root_quat_w, cube_2.data.root_quat_w), dim=1) def instance_randomize_cube_orientations_in_world_frame( env: ManagerBasedRLEnv, cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), - cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), ) -> torch.Tensor: """The orientation of the cubes in the world frame.""" if not hasattr(env, "rigid_objects_in_focus"): - return torch.full((env.num_envs, 9), fill_value=-1) + return torch.full((env.num_envs, 8), fill_value=-1) cube_1: RigidObjectCollection = env.scene[cube_1_cfg.name] cube_2: RigidObjectCollection = env.scene[cube_2_cfg.name] - cube_3: RigidObjectCollection = env.scene[cube_3_cfg.name] cube_1_quat_w = [] cube_2_quat_w = [] - cube_3_quat_w = [] for env_id in range(env.num_envs): cube_1_quat_w.append(cube_1.data.object_quat_w[env_id, env.rigid_objects_in_focus[env_id][0], :4]) cube_2_quat_w.append(cube_2.data.object_quat_w[env_id, env.rigid_objects_in_focus[env_id][1], :4]) - cube_3_quat_w.append(cube_3.data.object_quat_w[env_id, env.rigid_objects_in_focus[env_id][2], :4]) cube_1_quat_w = torch.stack(cube_1_quat_w) cube_2_quat_w = torch.stack(cube_2_quat_w) - cube_3_quat_w = torch.stack(cube_3_quat_w) - return torch.cat((cube_1_quat_w, cube_2_quat_w, cube_3_quat_w), dim=1) + return torch.cat((cube_1_quat_w, cube_2_quat_w), dim=1) def object_obs( env: ManagerBasedRLEnv, cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), - cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), ): """ @@ -114,18 +99,12 @@ def object_obs( cube_1 quat, cube_2 pos, cube_2 quat, - cube_3 pos, - cube_3 quat, gripper to cube_1, gripper to cube_2, - gripper to cube_3, cube_1 to cube_2, - cube_2 to cube_3, - cube_1 to cube_3, """ cube_1: RigidObject = env.scene[cube_1_cfg.name] cube_2: RigidObject = env.scene[cube_2_cfg.name] - cube_3: RigidObject = env.scene[cube_3_cfg.name] ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] cube_1_pos_w = cube_1.data.root_pos_w @@ -134,17 +113,17 @@ def object_obs( cube_2_pos_w = cube_2.data.root_pos_w cube_2_quat_w = cube_2.data.root_quat_w - cube_3_pos_w = cube_3.data.root_pos_w - cube_3_quat_w = cube_3.data.root_quat_w - ee_pos_w = ee_frame.data.target_pos_w[:, 0, :] gripper_to_cube_1 = cube_1_pos_w - ee_pos_w gripper_to_cube_2 = cube_2_pos_w - ee_pos_w - gripper_to_cube_3 = cube_3_pos_w - ee_pos_w cube_1_to_2 = cube_1_pos_w - cube_2_pos_w - cube_2_to_3 = cube_2_pos_w - cube_3_pos_w - cube_1_to_3 = cube_1_pos_w - cube_3_pos_w + + # Debug print: distance between cube_1 and cube_2 + xy_dist = torch.linalg.vector_norm(cube_1_to_2[:, :2], dim=1) + h_dist = torch.abs(cube_1_to_2[:, 2]) + print(f"[object_obs] cube_1_to_2 xy_dist: {xy_dist[0].item():.4f}, h_dist: {h_dist[0].item():.4f}, " + f"z_diff: {cube_1_to_2[0, 2].item():.4f}", flush=True) return torch.cat( ( @@ -152,14 +131,9 @@ def object_obs( cube_1_quat_w, cube_2_pos_w - env.scene.env_origins, cube_2_quat_w, - cube_3_pos_w - env.scene.env_origins, - cube_3_quat_w, gripper_to_cube_1, gripper_to_cube_2, - gripper_to_cube_3, cube_1_to_2, - cube_2_to_3, - cube_1_to_3, ), dim=1, ) @@ -169,7 +143,6 @@ def instance_randomize_object_obs( env: ManagerBasedRLEnv, cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), - cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), ): """ @@ -178,51 +151,36 @@ def instance_randomize_object_obs( cube_1 quat, cube_2 pos, cube_2 quat, - cube_3 pos, - cube_3 quat, gripper to cube_1, gripper to cube_2, - gripper to cube_3, cube_1 to cube_2, - cube_2 to cube_3, - cube_1 to cube_3, """ if not hasattr(env, "rigid_objects_in_focus"): - return torch.full((env.num_envs, 9), fill_value=-1) + return torch.full((env.num_envs, 23), fill_value=-1) cube_1: RigidObjectCollection = env.scene[cube_1_cfg.name] cube_2: RigidObjectCollection = env.scene[cube_2_cfg.name] - cube_3: RigidObjectCollection = env.scene[cube_3_cfg.name] ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] cube_1_pos_w = [] cube_2_pos_w = [] - cube_3_pos_w = [] cube_1_quat_w = [] cube_2_quat_w = [] - cube_3_quat_w = [] for env_id in range(env.num_envs): cube_1_pos_w.append(cube_1.data.object_pos_w[env_id, env.rigid_objects_in_focus[env_id][0], :3]) cube_2_pos_w.append(cube_2.data.object_pos_w[env_id, env.rigid_objects_in_focus[env_id][1], :3]) - cube_3_pos_w.append(cube_3.data.object_pos_w[env_id, env.rigid_objects_in_focus[env_id][2], :3]) cube_1_quat_w.append(cube_1.data.object_quat_w[env_id, env.rigid_objects_in_focus[env_id][0], :4]) cube_2_quat_w.append(cube_2.data.object_quat_w[env_id, env.rigid_objects_in_focus[env_id][1], :4]) - cube_3_quat_w.append(cube_3.data.object_quat_w[env_id, env.rigid_objects_in_focus[env_id][2], :4]) cube_1_pos_w = torch.stack(cube_1_pos_w) cube_2_pos_w = torch.stack(cube_2_pos_w) - cube_3_pos_w = torch.stack(cube_3_pos_w) cube_1_quat_w = torch.stack(cube_1_quat_w) cube_2_quat_w = torch.stack(cube_2_quat_w) - cube_3_quat_w = torch.stack(cube_3_quat_w) ee_pos_w = ee_frame.data.target_pos_w[:, 0, :] gripper_to_cube_1 = cube_1_pos_w - ee_pos_w gripper_to_cube_2 = cube_2_pos_w - ee_pos_w - gripper_to_cube_3 = cube_3_pos_w - ee_pos_w cube_1_to_2 = cube_1_pos_w - cube_2_pos_w - cube_2_to_3 = cube_2_pos_w - cube_3_pos_w - cube_1_to_3 = cube_1_pos_w - cube_3_pos_w return torch.cat( ( @@ -230,14 +188,9 @@ def instance_randomize_object_obs( cube_1_quat_w, cube_2_pos_w - env.scene.env_origins, cube_2_quat_w, - cube_3_pos_w - env.scene.env_origins, - cube_3_quat_w, gripper_to_cube_1, gripper_to_cube_2, - gripper_to_cube_3, cube_1_to_2, - cube_2_to_3, - cube_1_to_3, ), dim=1, ) @@ -355,6 +308,11 @@ def object_stacked( height_dist = torch.linalg.vector_norm(pos_diff[:, 2:], dim=1) xy_dist = torch.linalg.vector_norm(pos_diff[:, :2], dim=1) + # Debug print: distance between upper and lower objects + print(f"[object_stacked] xy_dist: {xy_dist[0].item():.4f} (thresh: {xy_threshold}), " + f"h_dist: {height_dist[0].item():.4f} (expected: {height_diff}, thresh: {height_threshold}), " + f"z_diff: {pos_diff[0, 2].item():.4f}", flush=True) + stacked = torch.logical_and(xy_dist < xy_threshold, (height_dist - height_diff) < height_threshold) if hasattr(env.scene, "surface_grippers") and len(env.scene.surface_grippers) > 0: @@ -395,7 +353,6 @@ def cube_poses_in_base_frame( env: ManagerBasedRLEnv, cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), - cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), return_key: Literal["pos", "quat", None] = None, ) -> torch.Tensor: @@ -403,15 +360,12 @@ def cube_poses_in_base_frame( cube_1: RigidObject = env.scene[cube_1_cfg.name] cube_2: RigidObject = env.scene[cube_2_cfg.name] - cube_3: RigidObject = env.scene[cube_3_cfg.name] pos_cube_1_world = cube_1.data.root_pos_w pos_cube_2_world = cube_2.data.root_pos_w - pos_cube_3_world = cube_3.data.root_pos_w quat_cube_1_world = cube_1.data.root_quat_w quat_cube_2_world = cube_2.data.root_quat_w - quat_cube_3_world = cube_3.data.root_quat_w robot: Articulation = env.scene[robot_cfg.name] root_pos_w = robot.data.root_pos_w @@ -423,12 +377,9 @@ def cube_poses_in_base_frame( pos_cube_2_base, quat_cube_2_base = math_utils.subtract_frame_transforms( root_pos_w, root_quat_w, pos_cube_2_world, quat_cube_2_world ) - pos_cube_3_base, quat_cube_3_base = math_utils.subtract_frame_transforms( - root_pos_w, root_quat_w, pos_cube_3_world, quat_cube_3_world - ) - pos_cubes_base = torch.cat((pos_cube_1_base, pos_cube_2_base, pos_cube_3_base), dim=1) - quat_cubes_base = torch.cat((quat_cube_1_base, quat_cube_2_base, quat_cube_3_base), dim=1) + pos_cubes_base = torch.cat((pos_cube_1_base, pos_cube_2_base), dim=1) + quat_cubes_base = torch.cat((quat_cube_1_base, quat_cube_2_base), dim=1) if return_key == "pos": return pos_cubes_base @@ -442,7 +393,6 @@ def object_abs_obs_in_base_frame( env: ManagerBasedRLEnv, cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), - cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), ): @@ -452,14 +402,11 @@ def object_abs_obs_in_base_frame( cube_1 quat, cube_2 pos, cube_2 quat, - cube_3 pos, - cube_3 quat, gripper pos, gripper quat, """ cube_1: RigidObject = env.scene[cube_1_cfg.name] cube_2: RigidObject = env.scene[cube_2_cfg.name] - cube_3: RigidObject = env.scene[cube_3_cfg.name] ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] robot: Articulation = env.scene[robot_cfg.name] @@ -472,18 +419,12 @@ def object_abs_obs_in_base_frame( cube_2_pos_w = cube_2.data.root_pos_w cube_2_quat_w = cube_2.data.root_quat_w - cube_3_pos_w = cube_3.data.root_pos_w - cube_3_quat_w = cube_3.data.root_quat_w - pos_cube_1_base, quat_cube_1_base = math_utils.subtract_frame_transforms( root_pos_w, root_quat_w, cube_1_pos_w, cube_1_quat_w ) pos_cube_2_base, quat_cube_2_base = math_utils.subtract_frame_transforms( root_pos_w, root_quat_w, cube_2_pos_w, cube_2_quat_w ) - pos_cube_3_base, quat_cube_3_base = math_utils.subtract_frame_transforms( - root_pos_w, root_quat_w, cube_3_pos_w, cube_3_quat_w - ) ee_pos_w = ee_frame.data.target_pos_w[:, 0, :] ee_quat_w = ee_frame.data.target_quat_w[:, 0, :] @@ -495,8 +436,6 @@ def object_abs_obs_in_base_frame( quat_cube_1_base, pos_cube_2_base, quat_cube_2_base, - pos_cube_3_base, - quat_cube_3_base, ee_pos_base, ee_quat_base, ), diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/terminations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/terminations.py index e306f9eb4a0a..f988a1f14dfe 100644 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/terminations.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/mdp/terminations.py @@ -26,35 +26,35 @@ def cubes_stacked( robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), cube_1_cfg: SceneEntityCfg = SceneEntityCfg("cube_1"), cube_2_cfg: SceneEntityCfg = SceneEntityCfg("cube_2"), - cube_3_cfg: SceneEntityCfg = SceneEntityCfg("cube_3"), xy_threshold: float = 0.04, height_threshold: float = 0.005, height_diff: float = 0.0468, atol=0.0001, rtol=0.0001, ): + """Check if cube_2 is stacked on cube_1.""" robot: Articulation = env.scene[robot_cfg.name] cube_1: RigidObject = env.scene[cube_1_cfg.name] cube_2: RigidObject = env.scene[cube_2_cfg.name] - cube_3: RigidObject = env.scene[cube_3_cfg.name] + # Position difference: cube_1 (lower) - cube_2 (upper) pos_diff_c12 = cube_1.data.root_pos_w - cube_2.data.root_pos_w - pos_diff_c23 = cube_2.data.root_pos_w - cube_3.data.root_pos_w # Compute cube position difference in x-y plane xy_dist_c12 = torch.norm(pos_diff_c12[:, :2], dim=1) - xy_dist_c23 = torch.norm(pos_diff_c23[:, :2], dim=1) # Compute cube height difference h_dist_c12 = torch.norm(pos_diff_c12[:, 2:], dim=1) - h_dist_c23 = torch.norm(pos_diff_c23[:, 2:], dim=1) - # Check cube positions - stacked = torch.logical_and(xy_dist_c12 < xy_threshold, xy_dist_c23 < xy_threshold) + # Debug print: distance between cube_2 and cube_1 + print(f"[cubes_stacked] xy_dist: {xy_dist_c12[0].item():.4f} (thresh: {xy_threshold}), " + f"h_dist: {h_dist_c12[0].item():.4f} (expected: {height_diff}, thresh: {height_threshold}), " + f"z_diff: {pos_diff_c12[0, 2].item():.4f}") + + # Check cube positions: cube_2 should be on top of cube_1 + stacked = xy_dist_c12 < xy_threshold stacked = torch.logical_and(h_dist_c12 - height_diff < height_threshold, stacked) - stacked = torch.logical_and(pos_diff_c12[:, 2] < 0.0, stacked) - stacked = torch.logical_and(h_dist_c23 - height_diff < height_threshold, stacked) - stacked = torch.logical_and(pos_diff_c23[:, 2] < 0.0, stacked) + stacked = torch.logical_and(pos_diff_c12[:, 2] < 0.0, stacked) # cube_1.z < cube_2.z means cube_2 is on top # Check gripper positions if hasattr(env.scene, "surface_grippers") and len(env.scene.surface_grippers) > 0: diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/stack_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/stack_env_cfg.py index 3af83de3a19a..8a6e27071031 100644 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/stack_env_cfg.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/stack_glassware/stack_env_cfg.py @@ -104,7 +104,7 @@ def __post_init__(self): class SubtaskCfg(ObsGroup): """Observations for subtask group.""" - grasp_1 = ObsTerm( + grasp = ObsTerm( func=mdp.object_grasped, params={ "robot_cfg": SceneEntityCfg("robot"), @@ -112,7 +112,7 @@ class SubtaskCfg(ObsGroup): "object_cfg": SceneEntityCfg("cube_2"), }, ) - stack_1 = ObsTerm( + stacked = ObsTerm( func=mdp.object_stacked, params={ "robot_cfg": SceneEntityCfg("robot"), @@ -120,14 +120,6 @@ class SubtaskCfg(ObsGroup): "lower_object_cfg": SceneEntityCfg("cube_1"), }, ) - grasp_2 = ObsTerm( - func=mdp.object_grasped, - params={ - "robot_cfg": SceneEntityCfg("robot"), - "ee_frame_cfg": SceneEntityCfg("ee_frame"), - "object_cfg": SceneEntityCfg("cube_3"), - }, - ) def __post_init__(self): self.enable_corruption = False @@ -153,12 +145,10 @@ class TerminationsCfg: func=mdp.root_height_below_minimum, params={"minimum_height": -0.05, "asset_cfg": SceneEntityCfg("cube_2")} ) - cube_3_dropping = DoneTerm( - func=mdp.root_height_below_minimum, params={"minimum_height": -0.05, "asset_cfg": SceneEntityCfg("cube_3")} + success = DoneTerm( + func=mdp.cubes_stacked ) - success = DoneTerm(func=mdp.cubes_stacked) - @configclass class StackEnvCfg(ManagerBasedRLEnvCfg): From 750b20f7fd627be314803ad1d79e41f73db660a2 Mon Sep 17 00:00:00 2001 From: LJones-alt Date: Wed, 25 Feb 2026 13:32:29 +0000 Subject: [PATCH 12/15] minor changes --- docker/.env.base | 3 +- docker/Dockerfile.base | 60 +- docs/find_best_checkpoints.py | 2 +- docs/run_experiments.sh | 57 +- .../bc_med_data_/20260122101438/config.json | 236 + .../bc_med_data_/20260122101438/config.json | 236 + .../bc_med_data_/20260122101438/config.json | 236 + .../bc_med_data_/20260122101438/config.json | 236 + .../bc_med_data_/20260122101438/config.json | 236 + get-pip.py | 27368 ++++++++++++++++ pyproject copy.toml | 100 + pyproject.toml | 164 +- regenerate_vial_collision.py | 85 + ros2/src | 1 + .../robomimic/backup_controller.py | 5 +- .../robomimic/backup_controller_handler.py | 62 +- .../robomimic/backup_controller_insert.py | 423 + .../robomimic/backup_controller_place.py | 456 + .../robomimic/evaluation.py | 6 +- scripts/imitation_learning/robomimic/play.py | 2 + .../robomimic/play_ensemble.py | 4 +- .../robomimic/play_ensemble_v05.py | 110 +- .../robomimic/play_with_backup.py | 4 +- .../robomimic/train_ensemble.py | 6 +- scripts/tools/regen_usd_file.py | 85 + scripts/tutorials/00_sim/create_empty.py | 2 +- source/chills | 1 + source/isaaclab/isaaclab/__init__.py | 2 +- source/isaaclab/isaaclab/envs/mdp/events.py | 1 + .../isaaclab/safety/switchingLogic.py | 17 +- source/isaaclab/pyproject.toml | 2 +- source/isaaclab/setup.py | 14 +- .../isaaclab_mimic/datagen/generation.py | 23 +- .../isaaclab_mimic/envs/__init__.py | 12 + .../isaaclab_mimic/envs/cube_mimic_env.py | 4 +- .../isaaclab_mimic/envs/cube_mimic_env_cfg.py | 7 +- .../isaaclab_mimic/envs/cube_mimic_vm_env.py | 189 + .../envs/cube_mimic_vm_env_cfg.py | 162 + .../cube_lift/config/franka/__init__.py | 47 + .../agents/robomimic/bc_rnn_low_dim.json | 8 +- .../franka/dev_ik_rel_env_insert_vismot.py | 218 + .../config/franka/dev_ik_rel_env_obs.py | 12 +- .../config/franka/dev_ik_rel_env_place.py | 19 +- .../franka/dev_ik_rel_env_place_vismot.py | 206 + .../config/franka/dev_ik_rel_vial_insert.py | 54 +- .../manipulation/cube_lift/lift_env_cfg.py | 43 +- .../cube_lift/mdp/franka_stack_events.py | 314 + .../cube_lift/mdp/observations.py | 10 +- .../cube_lift/mdp/terminations.py | 10 +- 49 files changed, 31281 insertions(+), 279 deletions(-) create mode 100644 docs/vision/model0/bc_med_data_/20260122101438/config.json create mode 100644 docs/vision/model1/bc_med_data_/20260122101438/config.json create mode 100644 docs/vision/model2/bc_med_data_/20260122101438/config.json create mode 100644 docs/vision/model3/bc_med_data_/20260122101438/config.json create mode 100644 docs/vision/model4/bc_med_data_/20260122101438/config.json create mode 100644 get-pip.py create mode 100644 pyproject copy.toml create mode 100644 regenerate_vial_collision.py create mode 160000 ros2/src create mode 100644 scripts/imitation_learning/robomimic/backup_controller_insert.py create mode 100644 scripts/imitation_learning/robomimic/backup_controller_place.py create mode 100644 scripts/tools/regen_usd_file.py create mode 160000 source/chills create mode 100755 source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_vm_env.py create mode 100644 source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_vm_env_cfg.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_insert_vismot.py create mode 100755 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place_vismot.py create mode 100644 source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/franka_stack_events.py diff --git a/docker/.env.base b/docker/.env.base index eceaf13699ad..a32eb7ef3c26 100644 --- a/docker/.env.base +++ b/docker/.env.base @@ -7,7 +7,7 @@ ACCEPT_EULA=Y # NVIDIA Isaac Sim base image ISAACSIM_BASE_IMAGE=nvcr.io/nvidia/isaac-sim # NVIDIA Isaac Sim version to use (e.g. 5.0.0) -ISAACSIM_VERSION=5.1.0 +ISAACSIM_VERSION=5.0.0 # Derived from the default path in the NVIDIA provided Isaac Sim container DOCKER_ISAACSIM_ROOT_PATH=/isaac-sim # The Isaac Lab path in the container @@ -17,3 +17,4 @@ DOCKER_USER_HOME=/root # Docker image and container name suffix (default "", set by the container_interface.py script) # Example: "-custom" DOCKER_NAME_SUFFIX="" + \ No newline at end of file diff --git a/docker/Dockerfile.base b/docker/Dockerfile.base index b87557035f3f..ccc6ab8ee10a 100644 --- a/docker/Dockerfile.base +++ b/docker/Dockerfile.base @@ -2,23 +2,23 @@ # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause - + # Nvidia Dockerfiles: https://github.com/NVIDIA-Omniverse/IsaacSim-dockerfiles # Please check above link for license information. - + # Base image ARG ISAACSIM_BASE_IMAGE_ARG ARG ISAACSIM_VERSION_ARG FROM ${ISAACSIM_BASE_IMAGE_ARG}:${ISAACSIM_VERSION_ARG} AS base ENV ISAACSIM_VERSION=${ISAACSIM_VERSION_ARG} - + # Set default RUN shell to bash SHELL ["/bin/bash", "-c"] - + # Adds labels to the Dockerfile LABEL version="2.1.1" LABEL description="Dockerfile for building and running the Isaac Lab framework inside Isaac Sim container image." - + # Arguments # Path to Isaac Sim root folder ARG ISAACSIM_ROOT_PATH_ARG @@ -29,13 +29,13 @@ ENV ISAACLAB_PATH=${ISAACLAB_PATH_ARG} # Home dir of docker user, typically '/root' ARG DOCKER_USER_HOME_ARG ENV DOCKER_USER_HOME=${DOCKER_USER_HOME_ARG} - + # Set environment variables ENV LANG=C.UTF-8 ENV DEBIAN_FRONTEND=noninteractive - + USER root - + # Install dependencies and remove cache RUN --mount=type=cache,target=/var/cache/apt \ apt-get update && apt-get install -y --no-install-recommends \ @@ -47,25 +47,25 @@ RUN --mount=type=cache,target=/var/cache/apt \ wget && \ apt -y autoremove && apt clean autoclean && \ rm -rf /var/lib/apt/lists/* - + # Copy the Isaac Lab directory (files to exclude are defined in .dockerignore) COPY ../ ${ISAACLAB_PATH} - + # Ensure isaaclab.sh has execute permissions RUN chmod +x ${ISAACLAB_PATH}/isaaclab.sh - + # Set up a symbolic link between the installed Isaac Sim root folder and _isaac_sim in the Isaac Lab directory RUN ln -sf ${ISAACSIM_ROOT_PATH} ${ISAACLAB_PATH}/_isaac_sim - + # Install toml dependency RUN ${ISAACLAB_PATH}/isaaclab.sh -p -m pip install toml - + # Install apt dependencies for extensions that declare them in their extension.toml RUN --mount=type=cache,target=/var/cache/apt \ ${ISAACLAB_PATH}/isaaclab.sh -p ${ISAACLAB_PATH}/tools/install_deps.py apt ${ISAACLAB_PATH}/source && \ apt -y autoremove && apt clean autoclean && \ rm -rf /var/lib/apt/lists/* - + # for singularity usage, have to create the directories that will binded RUN mkdir -p ${ISAACSIM_ROOT_PATH}/kit/cache && \ mkdir -p ${DOCKER_USER_HOME}/.cache/ov && \ @@ -75,7 +75,7 @@ RUN mkdir -p ${ISAACSIM_ROOT_PATH}/kit/cache && \ mkdir -p ${DOCKER_USER_HOME}/.nvidia-omniverse/logs && \ mkdir -p ${DOCKER_USER_HOME}/.local/share/ov/data && \ mkdir -p ${DOCKER_USER_HOME}/Documents - + # for singularity usage, create NVIDIA binary placeholders # NOTE: These are only needed for Singularity, not Docker # For Docker, comment these out as they override the real nvidia binaries @@ -87,12 +87,12 @@ RUN mkdir -p ${ISAACSIM_ROOT_PATH}/kit/cache && \ # touch /etc/localtime && \ # mkdir -p /var/run/nvidia-persistenced && \ # touch /var/run/nvidia-persistenced/socket - + # installing Isaac Lab dependencies # use pip caching to avoid reinstalling large packages RUN --mount=type=cache,target=${DOCKER_USER_HOME}/.cache/pip \ ${ISAACLAB_PATH}/isaaclab.sh --install - + # HACK: Remove install of quadprog dependency RUN ${ISAACLAB_PATH}/isaaclab.sh -p -m pip uninstall -y quadprog RUN set -euo pipefail && \ @@ -116,8 +116,8 @@ RUN set -euo pipefail && \ apt-get update && \ apt-get install -y --no-install-recommends cuda-toolkit-12-8 && \ apt-get -y autoremove && apt-get clean && rm -rf /var/lib/apt/lists/* - - + + ENV CUDA_HOME=/usr/local/cuda-12.8 ENV PATH=${CUDA_HOME}/bin:${PATH} ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH} @@ -135,19 +135,19 @@ RUN echo "export ISAACLAB_PATH=${ISAACLAB_PATH}" >> ${HOME}/.bashrc && \ echo "export TZ=$(date +%Z)" >> ${HOME}/.bashrc && \ echo "shopt -s histappend" >> /root/.bashrc && \ echo "PROMPT_COMMAND='history -a'" >> /root/.bashrc - + # use pip caching to avoid reinstalling large packages #RUN --mount=type=cache,target=${DOCKER_USER_HOME}/.cache/pip \ # pip install --upgrade --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128 # install TensorFlow and nightly PyTorch RUN --mount=type=cache,target=${DOCKER_USER_HOME}/.cache/pip \ - ${ISAACLAB_PATH}/_isaac_sim/python.sh -m pip install --upgrade --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128 - + ${ISAACLAB_PATH}/_isaac_sim/python.sh -m pip install --upgrade --pre torch torchvision torchaudio + RUN --mount=type=cache,target=${DOCKER_USER_HOME}/.cache/pip \ - ${ISAACLAB_PATH}/_isaac_sim/python.sh -m pip install tensorflow scikit-learn - -RUN --mount=type=cache,target=${DOCKER_USER_HOME}/.cache/pip \ -${ISAACLAB_PATH}/_isaac_sim/python.sh -m pip install protobuf==3.20.2 + ${ISAACLAB_PATH}/_isaac_sim/python.sh -m pip install tensorflow scikit-learn protobuf + +# RUN --mount=type=cache,target=${DOCKER_USER_HOME}/.cache/pip \ +# ${ISAACLAB_PATH}/_isaac_sim/python.sh -m pip install protobuf # Detect Ubuntu version and install CUDA 12.8 via NVIDIA network repo (cuda-keyring) RUN set -euo pipefail && \ . /etc/os-release && \ @@ -170,15 +170,15 @@ RUN set -euo pipefail && \ apt-get update && \ apt-get install -y --no-install-recommends cuda-toolkit-12-8 && \ apt-get -y autoremove && apt-get clean && rm -rf /var/lib/apt/lists/* - - + + ENV CUDA_HOME=/usr/local/cuda-12.8 ENV PATH=${CUDA_HOME}/bin:${PATH} ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH} ENV TORCH_CUDA_ARCH_LIST=8.0+PTX - + # Copy the Isaac Lab directory (files to exclude are defined in .dockerignore) COPY ../ ${ISAACLAB_PATH} # make working directory as the Isaac Lab directory # this is the default directory when the container is run -WORKDIR ${ISAACLAB_PATH} +WORKDIR ${ISAACLAB_PATH} \ No newline at end of file diff --git a/docs/find_best_checkpoints.py b/docs/find_best_checkpoints.py index bdda475fba0a..13b927f7e306 100644 --- a/docs/find_best_checkpoints.py +++ b/docs/find_best_checkpoints.py @@ -72,7 +72,7 @@ def main(): """Main function to process all model directories.""" # Define paths - handle both host and Docker container paths script_dir = Path(__file__).parent.resolve() - base_dir = script_dir / "place/Dev-IK-Rel-Place-v0" + base_dir = script_dir / "insert/Dev-IK-Rel-Insert-v0" output_dir = base_dir / "best_models" # Create output directory (including parent directories if needed) diff --git a/docs/run_experiments.sh b/docs/run_experiments.sh index 9f7da360e12a..9a526d7a878f 100644 --- a/docs/run_experiments.sh +++ b/docs/run_experiments.sh @@ -2,65 +2,52 @@ ## script to run experiments in the background -task="Dev-IK-Rel-Place-v0" -horizon=5000 +task="Dev-IK-Rel-v1" +horizon=500 num_rollouts=100 run_file="scripts/imitation_learning/robomimic/play_ensemble_v05.py" -ensemble_size=15 +exp_type="lift_none" ##### this now runs the experiments -## ensemble 15 -seed=101 -exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" -./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless +# ## ensemble 15 +# ensemble_size=15 +# seed=101 +# exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" +# ./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless -seed=107 -exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" -./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless +# seed=107 +# exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" +# ./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless -seed=115 -exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" -./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless +# seed=115 +# exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" +# ./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless -## ensemble 10 +## ensemble 10 ensemble_size=10 seed=101 -exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" +exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" ./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless seed=107 -exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" +exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" ./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless seed=115 -exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" +exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" ./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless -## ensemble 5 +## ensemble 5 ensemble_size=5 seed=101 -exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" -./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless - -seed=107 -exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" -./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless - -seed=115 -exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" -./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless - -## ensemble 1 -ensemble_size=1 -seed=101 -exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" +exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" ./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless seed=107 -exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" +exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" ./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless seed=115 -exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_highdem" +exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" ./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless diff --git a/docs/vision/model0/bc_med_data_/20260122101438/config.json b/docs/vision/model0/bc_med_data_/20260122101438/config.json new file mode 100644 index 000000000000..e25d634e013c --- /dev/null +++ b/docs/vision/model0/bc_med_data_/20260122101438/config.json @@ -0,0 +1,236 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_med_data_", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "pick_place_visuomotor" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": null, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": true, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": "Dev-IK-Rel-place-v0", + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + 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index 000000000000..e25d634e013c --- /dev/null +++ b/docs/vision/model1/bc_med_data_/20260122101438/config.json @@ -0,0 +1,236 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "bc_med_data_", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": true, + "wandb_proj_name": "pick_place_visuomotor" + }, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": null, + "epochs": [], + "on_best_validation": true, + "on_best_rollout_return": true, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": "Dev-IK-Rel-place-v0", + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": false, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0, + "terminate_on_success": true + }, + "env_meta_update_dict": {}, + "ckpt_path": null + }, + "train": { + "data": [ + { + 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"hp_values": [] + } +} \ No newline at end of file diff --git a/get-pip.py b/get-pip.py new file mode 100644 index 000000000000..22f29f881487 --- /dev/null +++ b/get-pip.py @@ -0,0 +1,27368 @@ +#!/usr/bin/env python +# +# Hi There! +# +# You may be wondering what this giant blob of binary data here is, you might +# even be worried that we're up to something nefarious (good for you for being +# paranoid!). This is a base85 encoding of a zip file, this zip file contains +# an entire copy of pip (version 25.3). +# +# Pip is a thing that installs packages, pip itself is a package that someone +# might want to install, especially if they're looking to run this get-pip.py +# script. Pip has a lot of code to deal with the security of installing +# packages, various edge cases on various platforms, and other such sort of +# "tribal knowledge" that has been encoded in its code base. Because of this +# we basically include an entire copy of pip inside this blob. We do this +# because the alternatives are attempt to implement a "minipip" that probably +# doesn't do things correctly and has weird edge cases, or compress pip itself +# down into a single file. +# +# If you're wondering how this is created, it is generated using +# `scripts/generate.py` in https://github.com/pypa/get-pip. + +import sys + +this_python = sys.version_info[:2] +min_version = (3, 9) +if this_python < min_version: + message_parts = [ + "This script does not work on Python {}.{}.".format(*this_python), + "The minimum supported Python version is {}.{}.".format(*min_version), + "Please use https://bootstrap.pypa.io/pip/{}.{}/get-pip.py instead.".format(*this_python), + ] + print("ERROR: " + " ".join(message_parts)) + sys.exit(1) + + +import os.path +import pkgutil +import shutil +import tempfile +import argparse +import importlib +from base64 import b85decode + + +def include_setuptools(args): + """ + Install setuptools only if absent, not excluded and when using Python <3.12. + """ + cli = not args.no_setuptools + env = not os.environ.get("PIP_NO_SETUPTOOLS") + absent = not importlib.util.find_spec("setuptools") + python_lt_3_12 = this_python < (3, 12) + return cli and env and absent and python_lt_3_12 + + +def include_wheel(args): + """ + Install wheel only if absent, not excluded and when using Python <3.12. + """ + cli = not args.no_wheel + env = not os.environ.get("PIP_NO_WHEEL") + absent = not importlib.util.find_spec("wheel") + python_lt_3_12 = this_python < (3, 12) + return cli and env and absent and python_lt_3_12 + + +def determine_pip_install_arguments(): + pre_parser = argparse.ArgumentParser() + pre_parser.add_argument("--no-setuptools", action="store_true") + pre_parser.add_argument("--no-wheel", action="store_true") + pre, args = pre_parser.parse_known_args() + + args.append("pip") + + if include_setuptools(pre): + args.append("setuptools") + + if include_wheel(pre): + args.append("wheel") + + return ["install", "--upgrade", "--force-reinstall"] + args + + +def monkeypatch_for_cert(tmpdir): + """Patches `pip install` to provide default certificate with the lowest priority. + + This ensures that the bundled certificates are used unless the user specifies a + custom cert via any of pip's option passing mechanisms (config, env-var, CLI). + + A monkeypatch is the easiest way to achieve this, without messing too much with + the rest of pip's internals. + """ + from pip._internal.commands.install import InstallCommand + + # We want to be using the internal certificates. + cert_path = os.path.join(tmpdir, "cacert.pem") + with open(cert_path, "wb") as cert: + cert.write(pkgutil.get_data("pip._vendor.certifi", "cacert.pem")) + + install_parse_args = InstallCommand.parse_args + + def cert_parse_args(self, args): + if not self.parser.get_default_values().cert: + # There are no user provided cert -- force use of bundled cert + self.parser.defaults["cert"] = cert_path # calculated above + return install_parse_args(self, args) + + InstallCommand.parse_args = cert_parse_args + + +def bootstrap(tmpdir): + monkeypatch_for_cert(tmpdir) + + # Execute the included pip and use it to install the latest pip and + # any user-requested packages from PyPI. + from pip._internal.cli.main import main as pip_entry_point + args = determine_pip_install_arguments() + sys.exit(pip_entry_point(args)) + + +def main(): + tmpdir = None + try: + # Create a temporary working directory + tmpdir = tempfile.mkdtemp() + + # Unpack the zipfile into the temporary directory + pip_zip = os.path.join(tmpdir, "pip.zip") + with open(pip_zip, "wb") as fp: + fp.write(b85decode(DATA.replace(b"\n", b""))) + + # Add the zipfile to sys.path so that we can import it + sys.path.insert(0, pip_zip) + + # Run the bootstrap + bootstrap(tmpdir=tmpdir) + finally: + # Clean up our temporary working directory + if tmpdir: + shutil.rmtree(tmpdir, ignore_errors=True) + + +DATA = b""" 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+831r;a4%nWWo~3|axZmqY;0*_GcRyqV{2h&WpgiIUukY>bYEXCaCuNm0Rj{Q6aWAK2ms3fSzBC`8LTE +D003!+001KZ0000000000005+ckqa3BaA|NaUv_0~WN&gWb#iQMX<{=kaA9L>VP|D?FLP;lE^v8JO92 +8D0~7!N00;of09jiA00002000000000o00000000000001_f%q&L0B~t=FJE?LZe(wAFLiQkY-wUMFK +}UFYhh<)b1!0HV{344a&&VqUtei%X>?y-E^v8JO928D0~7!N00;of09jk!xMQD1^@ux6aWA^00000000000001_f#NS20B~t=FJE?LZe(wAFLiQkY-wU +MFK}UFYhh<)b1!0HV{344a&&VqcV%H~a%E;;W@&C=Y-xIBE^v8JO928D0~7!N00;of09jk+ikX_&0RR +Al1ONae00000000000001_fyFi%0B~t=FJE?LZe(wAFLiQkY-wUMFLiWjY%gD5X>MtBUtcb8c~DCM0u +%!j000080LuVbTkNXj51I!603{Ou044wc00000000000HlH6I2izNX>c!Jc4cm4Z*nhna%^mAVlyvwb +ZKlaV{dM5Wn*+{Z*DGdc~DCM0u%!j000080LuVbTMExy2VMdI07V7>03iSX00000000000HlGvKp6mV +X>c!Jc4cm4Z*nhna%^mAVlyvwbZKlaaB^>Wc`k5yP)h*<6ay3h000O8%K%whP6PIz`~Uy|@&Nz?X>2cXb!ByBE^v8JO928D0~7!N00;of09j +i%j&1R)1pok@4*&oo00000000000001_ft5rV0B~t=FJE?LZe(wAFLiQkY-wUMFLiWjY%g+Uadl;LbS +`jtP)h*<6ay3h000O8%K%whHYnA!U?X>2cYWpi+EZgXWWaCuNm0Rj{Q6aWAK2ms3fSzBKfFZT);001IZ001EX0000000000005+c +BTyLtaA|NaUv_0~WN&gWb#iQMX<{=kb#!TLFLGsca(OOrc~DCM0u%!j000080LuVbTR1R}=I#~%05n7 +Z03ZMW00000000000HlFzXBhx+X>c!Jc4cm4Z*nhna%^mAVlyvwbZKlab8~E8E^v8JO928D0~7!N00; +of09jic!Jc4cm4Z*nhna%^mAVlyvwbZKlabZKp6Z*_DoaCuNm0Rj{ +Q6aWAK2ms3fSz9aB=w25S007uG0018V0000000000005+cIh+{)aA|NaUv_0~WN&gWb#iQMX<{=kb#! +TLFLiQkE^v8JO928D0~7!N00;of09jjB)K=BM2LJ#Y6#xJr00000000000001_fsL;j0B~t=FJE?LZe +(wAFLiQkY-wUMFLiWjY%h0VX>=}dc~DCQ1^@s60J;IX0sn^p0E)L60000 +""" + + +if __name__ == "__main__": + main() diff --git a/pyproject copy.toml b/pyproject copy.toml new file mode 100644 index 000000000000..aa5574018eb0 --- /dev/null +++ b/pyproject copy.toml @@ -0,0 +1,100 @@ +[tool.isort] + +py_version = 310 +line_length = 120 +group_by_package = true + +# Files to skip +skip_glob = ["docs/*", "logs/*", "_isaac_sim/*", ".vscode/*"] + +# Order of imports +sections = [ + "FUTURE", + "STDLIB", + "THIRDPARTY", + "ASSETS_FIRSTPARTY", + "FIRSTPARTY", + "EXTRA_FIRSTPARTY", + "TASK_FIRSTPARTY", + "LOCALFOLDER", +] + +# Extra standard libraries considered as part of python (permissive licenses +extra_standard_library = [ + "numpy", + "h5py", + "open3d", + "torch", + "tensordict", + "bpy", + "matplotlib", + "gymnasium", + "gym", + "scipy", + "hid", + "yaml", + "prettytable", + "toml", + "trimesh", + "tqdm", + "torchvision", + "transformers", + "einops" # Needed for transformers, doesn't always auto-install +] +# Imports from Isaac Sim and Omniverse +known_third_party = [ + "isaacsim.core.api", + "isaacsim.replicator.common", + "omni.replicator.core", + "pxr", + "omni.kit.*", + "warp", + "carb", + "Semantics", +] +# Imports from this repository +known_first_party = "isaaclab" +known_assets_firstparty = "isaaclab_assets" +known_extra_firstparty = [ + "isaaclab_rl", + "isaaclab_mimic", +] +known_task_firstparty = "isaaclab_tasks" +# Imports from the local folder +known_local_folder = "config" + +[tool.pyright] + +include = ["source", "scripts"] +exclude = [ + "**/__pycache__", + "**/_isaac_sim", + "**/docs", + "**/logs", + ".git", + ".vscode", +] + +typeCheckingMode = "basic" +pythonVersion = "3.11" +pythonPlatform = "Linux" +enableTypeIgnoreComments = true + +# This is required as the CI pre-commit does not download the module (i.e. numpy, torch, prettytable) +# Therefore, we have to ignore missing imports +reportMissingImports = "none" +# This is required to ignore for type checks of modules with stubs missing. +reportMissingModuleSource = "none" # -> most common: prettytable in mdp managers + +reportGeneralTypeIssues = "none" # -> raises 218 errors (usage of literal MISSING in dataclasses) +reportOptionalMemberAccess = "warning" # -> raises 8 errors +reportPrivateUsage = "warning" + + +[tool.codespell] +skip = '*.usd,*.svg,*.png,_isaac_sim*,*.bib,*.css,*/_build' +quiet-level = 0 +# the world list should always have words in lower case +ignore-words-list = "haa,slq,collapsable,buss,reacher" +# todo: this is hack to deal with incorrect spelling of "Environment" in the Isaac Sim grid world asset +exclude-file = "source/isaaclab/isaaclab/sim/spawners/from_files/from_files.py" diff --git a/pyproject.toml b/pyproject.toml index aa5574018eb0..01661906b2e9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,67 +1,104 @@ -[tool.isort] - -py_version = 310 -line_length = 120 -group_by_package = true - -# Files to skip -skip_glob = ["docs/*", "logs/*", "_isaac_sim/*", ".vscode/*"] - -# Order of imports -sections = [ - "FUTURE", - "STDLIB", - "THIRDPARTY", - "ASSETS_FIRSTPARTY", - "FIRSTPARTY", - "EXTRA_FIRSTPARTY", - "TASK_FIRSTPARTY", - "LOCALFOLDER", +# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +[tool.ruff] +line-length = 120 +target-version = "py310" + +# Exclude directories +extend-exclude = [ + "logs", + "_isaac_sim", + ".vscode", + "_*", + ".git", +] + +[tool.ruff.lint] +# Enable flake8 rules and other useful ones +select = [ + "E", # pycodestyle errors + "W", # pycodestyle warnings + "F", # pyflakes + "I", # isort + "UP", # pyupgrade + "C90", # mccabe complexity + # "D", # pydocstyle + "SIM", # flake8-simplify + "RET", # flake8-return ] -# Extra standard libraries considered as part of python (permissive licenses -extra_standard_library = [ - "numpy", - "h5py", - "open3d", - "torch", - "tensordict", - "bpy", - "matplotlib", - "gymnasium", - "gym", - "scipy", - "hid", - "yaml", - "prettytable", - "toml", - "trimesh", - "tqdm", - "torchvision", - "transformers", - "einops" # Needed for transformers, doesn't always auto-install +# Ignore specific rules (matching your flake8 config) +ignore = [ + "E402", # Module level import not at top of file + "D401", # First line should be in imperative mood + "RET504", # Unnecessary variable assignment before return statement + "RET505", # Unnecessary elif after return statement + "SIM102", # Use a single if-statement instead of nested if-statements + "SIM103", # Return the negated condition directly + "SIM108", # Use ternary operator instead of if-else statement + "SIM117", # Merge with statements for context managers + "SIM118", # Use {key} in {dict} instead of {key} in {dict}.keys() + "UP006", # Use 'dict' instead of 'Dict' type annotation + "UP018", # Unnecessary `float` call (rewrite as a literal) ] -# Imports from Isaac Sim and Omniverse -known_third_party = [ - "isaacsim.core.api", - "isaacsim.replicator.common", - "omni.replicator.core", + +[tool.ruff.lint.per-file-ignores] +"__init__.py" = ["F401"] # Allow unused imports in __init__.py files + +[tool.ruff.lint.mccabe] +max-complexity = 30 + +[tool.ruff.lint.pydocstyle] +convention = "google" + +[tool.ruff.lint.isort] + +# Custom import sections with separate sections for each Isaac Lab extension +section-order = [ + "future", + "standard-library", + "third-party", + # Group omniverse extensions separately since they are run-time dependencies + # which are pulled in by Isaac Lab extensions + "omniverse-extensions", + # Group Isaac Lab extensions together since they are all part of the Isaac Lab project + "isaaclab", + "isaaclab-contrib", + "isaaclab-rl", + "isaaclab-mimic", + "isaaclab-tasks", + "isaaclab-assets", + # First-party is reserved for project templates + "first-party", + "local-folder", +] + +[tool.ruff.lint.isort.sections] +# Define what belongs in each custom section + +"omniverse-extensions" = [ + "isaacsim", + "omni", "pxr", - "omni.kit.*", - "warp", "carb", + "usdrt", "Semantics", + "curobo", ] -# Imports from this repository -known_first_party = "isaaclab" -known_assets_firstparty = "isaaclab_assets" -known_extra_firstparty = [ - "isaaclab_rl", - "isaaclab_mimic", -] -known_task_firstparty = "isaaclab_tasks" -# Imports from the local folder -known_local_folder = "config" + +"isaaclab" = ["isaaclab"] +"isaaclab-assets" = ["isaaclab_assets"] +"isaaclab-contrib" = ["isaaclab_contrib"] +"isaaclab-rl" = ["isaaclab_rl"] +"isaaclab-mimic" = ["isaaclab_mimic"] +"isaaclab-tasks" = ["isaaclab_tasks"] + +[tool.ruff.format] + +docstring-code-format = true [tool.pyright] @@ -92,9 +129,14 @@ reportPrivateUsage = "warning" [tool.codespell] -skip = '*.usd,*.svg,*.png,_isaac_sim*,*.bib,*.css,*/_build' +skip = '*.usd,*.usda,*.usdz,*.svg,*.png,_isaac_sim*,*.bib,*.css,*/_build' quiet-level = 0 # the world list should always have words in lower case -ignore-words-list = "haa,slq,collapsable,buss,reacher" -# todo: this is hack to deal with incorrect spelling of "Environment" in the Isaac Sim grid world asset -exclude-file = "source/isaaclab/isaaclab/sim/spawners/from_files/from_files.py" +ignore-words-list = "haa,slq,collapsable,buss,reacher,thirdparty" + + +[tool.pytest.ini_options] + +markers = [ + "isaacsim_ci: mark test to run in isaacsim ci", +] \ No newline at end of file diff --git a/regenerate_vial_collision.py b/regenerate_vial_collision.py new file mode 100644 index 000000000000..d9058e6a0564 --- /dev/null +++ b/regenerate_vial_collision.py @@ -0,0 +1,85 @@ +#!/usr/bin/env python3 +"""Script to regenerate USD files for vial rack and capped vial with proper collision meshes.""" + +from isaaclab.app import AppLauncher + +# Launch Isaac Sim +app_launcher = AppLauncher(headless=True) +simulation_app = app_launcher.app + +import os +from isaaclab.sim.converters import MeshConverter, MeshConverterCfg +from isaaclab.sim.schemas import schemas_cfg +from isaaclab.utils.dict import print_dict + +def regenerate_vial_rack(): + """Regenerate vial rack USD with convex decomposition.""" + print("=" * 80) + print("Regenerating Vial Rack USD with Convex Decomposition") + print("=" * 80) + + mesh_converter_cfg = MeshConverterCfg( + asset_path="/workspace/isaaclab/source/isaaclab_assets/isaaclab_assets/glassware/vial_rack/vial_rack.obj", + usd_dir="/workspace/isaaclab/source/isaaclab_assets/isaaclab_assets/glassware/vial_rack", + usd_file_name="vial_rack.usd", + force_usd_conversion=True, + make_instanceable=True, + mass_props=schemas_cfg.MassPropertiesCfg(mass=0.1), + rigid_props=schemas_cfg.RigidBodyPropertiesCfg(), + collision_props=schemas_cfg.CollisionPropertiesCfg(collision_enabled=True), + mesh_collision_props=schemas_cfg.ConvexDecompositionPropertiesCfg( + max_convex_hulls=64, + voxel_resolution=1000000, + hull_vertex_limit=64, + min_thickness=0.0005 + ), + translation=(0.0, 0.0, 0.0), + rotation=(1.0, 0.0, 0.0, 0.0), + scale=(1.0, 1.0, 1.0), + ) + + print("Config:") + print_dict(mesh_converter_cfg.to_dict(), nesting=0) + + mesh_converter = MeshConverter(mesh_converter_cfg) + print(f"✓ Generated: {mesh_converter.usd_path}") + print() + +def regenerate_capped_vial(): + """Regenerate capped vial USD with convex hull.""" + print("=" * 80) + print("Regenerating Capped Vial USD with Convex Hull") + print("=" * 80) + + mesh_converter_cfg = MeshConverterCfg( + asset_path="/workspace/isaaclab/source/isaaclab_assets/isaaclab_assets/glassware/vial_with_cap/vial_with_cap.obj", + usd_dir="/workspace/isaaclab/source/isaaclab_assets/isaaclab_assets/glassware/vial_with_cap", + usd_file_name="vial_with_cap.usd", + force_usd_conversion=True, + make_instanceable=True, + mass_props=schemas_cfg.MassPropertiesCfg(mass=0.01), + rigid_props=schemas_cfg.RigidBodyPropertiesCfg(), + collision_props=schemas_cfg.CollisionPropertiesCfg(collision_enabled=True), + mesh_collision_props=schemas_cfg.ConvexHullPropertiesCfg( + hull_vertex_limit=32, + min_thickness=0.0005 + ), + translation=(0.0, 0.0, 0.0), + rotation=(1.0, 0.0, 0.0, 0.0), + scale=(1.0, 1.0, 1.0), + ) + + print("Config:") + print_dict(mesh_converter_cfg.to_dict(), nesting=0) + + mesh_converter = MeshConverter(mesh_converter_cfg) + print(f"✓ Generated: {mesh_converter.usd_path}") + print() + +if __name__ == "__main__": + regenerate_vial_rack() + regenerate_capped_vial() + print("=" * 80) + print("✓ All USD files regenerated successfully!") + print("=" * 80) + simulation_app.close() diff --git a/ros2/src b/ros2/src new file mode 160000 index 000000000000..a739caa35161 --- /dev/null +++ b/ros2/src @@ -0,0 +1 @@ +Subproject commit a739caa35161035f3a4505fb077068e9adf51172 diff --git a/scripts/imitation_learning/robomimic/backup_controller.py b/scripts/imitation_learning/robomimic/backup_controller.py index 6d63069dd340..f529e417e227 100644 --- a/scripts/imitation_learning/robomimic/backup_controller.py +++ b/scripts/imitation_learning/robomimic/backup_controller.py @@ -226,7 +226,7 @@ def infer_state_machine( pose_pos = wp.transform_get_translation(final_object_pose[tid]) pose_rot = wp.transform_get_rotation(final_object_pose[tid]) # Apply offset in x-direction (5 cm = 0.05 m) - offset_pos = wp.vec3(pose_pos.x, pose_pos.y+0.1, pose_pos.z+0.02) + offset_pos = wp.vec3(pose_pos.x, pose_pos.y+0.1, pose_pos.z+0.01) des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) gripper_state[tid] = GripperState.CLOSE # wait for a while @@ -411,9 +411,10 @@ def compute(self, ee_pose: torch.Tensor, start_object_pose: torch.Tensor, curren dist = np.linalg.norm(cur_pos - des_pos) # print(f"StateMachine debug state {debug_state}") # print(f"[Env {i}]") + # print(f" Goal pose : {final_object_pose}") # print(f" Current -> pos: {cur_pos}, quat: {cur_quat}") # print(f" Desired -> pos: {des_pos}, quat: {des_quat}") - #print(f" Euclidean distance: {dist:.4f}") + # print(f" Euclidean distance: {dist:.4f}") #print(f"returned state : {self.sm_state}") # convert transformations back to (w, x, y, z) des_ee_pose = self.des_ee_pose[:, [0, 1, 2, 6, 3, 4, 5]] diff --git a/scripts/imitation_learning/robomimic/backup_controller_handler.py b/scripts/imitation_learning/robomimic/backup_controller_handler.py index e3b336e1bfd3..f6e63d51df63 100644 --- a/scripts/imitation_learning/robomimic/backup_controller_handler.py +++ b/scripts/imitation_learning/robomimic/backup_controller_handler.py @@ -4,17 +4,24 @@ from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg, BinaryJointPositionActionCfg from isaaclab.envs.mdp.actions.binary_joint_actions import BinaryJointPositionAction from backup_controller import BackupControllerSM +from backup_controller_place import BackupControllerPlaceSM +from backup_controller_insert import BackupControllerInsertSM import torch class BackupController: - def __init__(self,env, device): + def __init__(self,env, device, tasktype="lift"): self.env = env self.sim = env.unwrapped.sim self.num_envs = env.num_envs self.device = device + self.tasktype = tasktype self.rest_pos = self._get_rest_pos() - self.backup_controller = BackupControllerSM(0.01*2, self.num_envs,'cuda' , 0.02) + self.goal_quat = self._get_goal_pos() + self.object_goal_pos = self._get_goal_pos() + self.object_goal_rot = self._get_goal_rot() + self.object_start_pose = self._get_start_pos() + self.backup_controller = self._get_controller()#BackupControllerSM(0.01*2, self.num_envs,'cuda' , 0.02) self.robot = env.unwrapped.scene["robot"] self.robot_entity_cfg , self.ee_jacobi_idx = self._setup_robot() self.state_guess = 0 @@ -22,10 +29,50 @@ def __init__(self,env, device): self.clamp = 0.05 self.rot_gain = 1.0 self.rot_clamp = 0.5 - self.object_start_pose = self._get_start_pos() - + + self.reset() + def _get_controller(self): + match self.tasktype: + case "lift": + return BackupControllerSM(0.01*2, self.num_envs,'cuda' , 0.02) + case "place": + print(f"[DEBUG] Using Place Backup Controller") + return BackupControllerPlaceSM(dt=0.01*2, num_envs=self.num_envs, position_threshold=0.02, device='cuda', offset = torch.tensor([[-0.1, 0, 0.1]], device='cuda:0'), goal_quat = self.object_goal_rot, goal_pos= self.object_goal_pos) + case "insert": + return BackupControllerInsertSM(0.01*2, self.num_envs,'cuda' , 0.02) + case _: + raise ValueError(f"Unknown task type {self.tasktype} for backup controller") + + def _get_goal_pos(self): + match self.tasktype: + case "lift": + goal_rot = self.env.unwrapped.command_manager.get_command("object_pose") + # print(f"[DEBUG] Getting goal quat for lift task {goal_rot}") + return self.env.unwrapped.command_manager.get_command("object_pose") + case "place": + goal_pos = self.env.unwrapped.scene["scale"].data.root_pose_w + # print(f"[DEBUG] Getting goal pos for place task {goal_pos}") + return self.env.unwrapped.scene["scale"].data.root_pose_w + + case "insert": + return self.env.unwrapped.scene["vialrack"].data.root_pose_w + + def _get_goal_rot(self): + match self.tasktype: + case "lift": + goal_rot = self.env.unwrapped.command_manager.get_command("object_pose") + print(f"[DEBUG] Getting goal quat for lift task {goal_rot}") + return self.env.unwrapped.command_manager.get_command("object_pose") + case "place": + goal_rot = self.env.unwrapped.command_manager.get_command("object_pose") + print(f"[DEBUG] Getting goal quat for place task {goal_rot}") + return self.env.unwrapped.command_manager.get_command("object_pose") + + case "insert": + return self.env.unwrapped.scene["vialrack"].data.root_pose_w + def reset(self): self.backup_controller.reset_idx() @@ -33,6 +80,8 @@ def _get_start_pos(self): object_pose_w = self.env.unwrapped.scene["object"].data.root_pose_w return object_pose_w + + def _get_rest_pos(self): rest_pos = torch.tensor([[0.5206, 0.0096, 0.3751]], device=self.device) ee_recovery_rot = torch.tensor([[ 0.6664, 0.0360, 0.7414, -0.0705]], device=self.device) @@ -64,8 +113,9 @@ def get_controller_action(self, state_guess, env_num): root_pose_w[:, 0:3], root_pose_w[:, 3:7], ee_pose_w[:, 0:3], ee_pose_w[:, 3:7] ) - object_pose = self.env.unwrapped.command_manager.get_command("object_pose") - ee_recovery, gripper , state_guess= self.backup_controller.compute(ee_pose =ee_pose_w, start_object_pose = self.object_start_pose, current_object_pose=current_object_pose, final_object_pose=object_pose, rest_pose=self.rest_pos, sm_state=state_guess) + + #object_pose = self.env.unwrapped.command_manager.get_command("object_pose") + ee_recovery, gripper , state_guess= self.backup_controller.compute(ee_pose =ee_pose_w, start_object_pose = self.object_start_pose, current_object_pose=current_object_pose, final_object_pose=self.object_goal_pos, rest_pose=self.rest_pos, sm_state=state_guess) #print(f"[BAKCUP] state {state_guess}") #return ee_recovery , gripper , state_guess diff --git a/scripts/imitation_learning/robomimic/backup_controller_insert.py b/scripts/imitation_learning/robomimic/backup_controller_insert.py new file mode 100644 index 000000000000..5c12bdd60aea --- /dev/null +++ b/scripts/imitation_learning/robomimic/backup_controller_insert.py @@ -0,0 +1,423 @@ +import warp as wp +import gymnasium as gym +import torch +from collections.abc import Sequence +import numpy as np + +# this is a backup state machine controller +# initialize warp +wp.init() + + +class GripperState: + """States for the gripper.""" + + OPEN = wp.constant(1.0) + CLOSE = wp.constant(-1.0) + +class BackupSMWaitTime: + """Additional wait times (in s) for states for before switching.""" + + REST = wp.constant(0.5) + APPROACH_ABOVE_OBJECT = wp.constant(0.3) + APPROACH_OBJECT = wp.constant(0.3) + GRASP_OBJECT = wp.constant(0.1) # 1 second wait + 0.3 for gripper to close + LIFT_OBJECT = wp.constant(0.3) + MIDPOINT = wp.constant(0.1) + APPROACH_ABOVE_GOAL = wp.constant(0.1) + APPROACH_GOAL = wp.constant(6) + +class BackupSM: + """States for the pick state machine.""" + + REST = wp.constant(0) + APPROACH_ABOVE_OBJECT = wp.constant(1) + APPROACH_OBJECT = wp.constant(2) + GRASP_OBJECT = wp.constant(3) + LIFT_OBJECT = wp.constant(4) + MIDPOINT = wp.constant(5) + APPROACH_ABOVE_GOAL = wp.constant(6) + APPROACH_GOAL = wp.constant(7) + #UNGRASP_OBJECT = wp.constant(7) + +@wp.func +def distance_below_threshold(current_pos: wp.vec3, desired_pos: wp.vec3, threshold: float) -> bool: + return wp.length(current_pos - desired_pos) < threshold + + +@wp.kernel +def infer_state_machine( + dt: wp.array(dtype=float), + sm_state: wp.array(dtype=int), + sm_wait_time: wp.array(dtype=float), + ee_pose: wp.array(dtype=wp.transform), # current ee pose + object_pose: wp.array(dtype=wp.transform), # start object location + current_object_pose: wp.array(dtype=wp.transform), # current object pose + final_object_pose: wp.array(dtype=wp.transform), + rest_ee_pose : wp.array(dtype=wp.transform), # rest position + des_ee_pose: wp.array(dtype=wp.transform), # what we return + gripper_state: wp.array(dtype=float), + offset: wp.array(dtype=wp.transform), + position_threshold: float, + debug_des_pose: wp.array(dtype=wp.transform), # NEW + debug_cur_pose: wp.array(dtype=wp.transform), # NEW + debug_state: wp.array(dtype=int), # New +): + # retrieve thread id + tid = wp.tid() + # retrieve state machine state + state = sm_state[tid] + # decide next state + if state == BackupSM.REST: + # First lift up to a safe height, then go to rest position + current_ee_pos = wp.transform_get_translation(ee_pose[tid]) + rest_pos = wp.transform_get_translation(rest_ee_pose[tid]) + rest_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + + # If we're below safe height, first move up + safe_height = 0.35 # Safe z-height to avoid collisions + if current_ee_pos.z < safe_height: + # Lift straight up first + lift_pos = wp.vec3(current_ee_pos.x, current_ee_pos.y, safe_height) + des_ee_pose[tid] = wp.transform(lift_pos, rest_rot) + else: + # Already at safe height, move to rest position + des_ee_pose[tid] = rest_ee_pose[tid] + + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(rest_ee_pose[tid]), + 0.02, + ): + if sm_wait_time[tid] >= BackupSMWaitTime.REST: + print("[SM_INFO] : Moving from REST to APPROACH_ABOVE_OBJECT") + # move to next state and reset wait time + sm_state[tid] = BackupSM.APPROACH_ABOVE_OBJECT + sm_wait_time[tid] = 0.0 + elif state == BackupSM.APPROACH_ABOVE_OBJECT: + # print("[SM] APPR above obj") + pose_pos = wp.transform_get_translation(current_object_pose[tid]) + pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + # Go to a safe height ABOVE the object (z + 0.15m) with x offset + safe_above_pos = wp.vec3(pose_pos.x - 0.1, pose_pos.y, pose_pos.z + 0.15) + des_ee_pose[tid] = wp.transform(safe_above_pos, pose_rot) + gripper_state[tid] = GripperState.OPEN # Open gripper in preparation + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + position_threshold, + ): + # wait for a while + if sm_wait_time[tid] >= BackupSMWaitTime.APPROACH_ABOVE_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from APPR_ABOVE to APPROACH_OBJECT") + sm_state[tid] = BackupSM.APPROACH_OBJECT + sm_wait_time[tid] = 0.0 + elif state == BackupSM.APPROACH_OBJECT: + # print("[SM] approach object") + + pose_pos = wp.transform_get_translation(current_object_pose[tid]) + pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + # Apply offset in x-direction (5 cm = 0.05 m) + offset_pos = wp.vec3(pose_pos.x -0.1, pose_pos.y+0.02, pose_pos.z+0.02) #sample vial + 0.04 + des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) + gripper_state[tid] = GripperState.OPEN + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + position_threshold, + ): + if sm_wait_time[tid] >= BackupSMWaitTime.APPROACH_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from APPR_OBJ to GRASP_OBJECT") + sm_state[tid] = BackupSM.GRASP_OBJECT + sm_wait_time[tid] = 0.0 + elif state == BackupSM.GRASP_OBJECT: + # print("[SM] GRASP") + pose_pos = wp.transform_get_translation(current_object_pose[tid]) + pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + # Stay at the approach position while closing gripper + offset_pos = wp.vec3(pose_pos.x - 0.1, pose_pos.y + 0.02, pose_pos.z + 0.02) + des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if sm_wait_time[tid] >= BackupSMWaitTime.GRASP_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from GRSP to LIFT_OBJECT") + sm_state[tid] = BackupSM.LIFT_OBJECT + sm_wait_time[tid] = 0.0 + + elif state == BackupSM.LIFT_OBJECT: + #print("[SM] LIFT") # uses starting position + pose_pos = wp.transform_get_translation(object_pose[tid]) + pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + # Apply offset in x-direction (5 cm = 0.05 m) + offset_pos = wp.vec3(pose_pos.x -0.1, pose_pos.y+0.02, pose_pos.z+0.2) #sample vial + 0.04 + des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + 0.03, + ): + # wait for a while + if sm_wait_time[tid] >= BackupSMWaitTime.LIFT_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from LIFT to MIDPOINT") + sm_state[tid] = BackupSM.MIDPOINT + sm_wait_time[tid] = 0.0 + elif state == BackupSM.MIDPOINT: + #print("[SM] MIDPOINT") + pose_pos = wp.transform_get_translation(rest_ee_pose[tid]) + pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + # Apply offset in x-direction (5 cm = 0.05 m) + #offset_pos = wp.vec3(pose_pos.x -0.1, pose_pos.y+0.02, pose_pos.z+0.2) #sample vial + 0.04 + des_ee_pose[tid] = rest_ee_pose[tid] + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + 0.03, + ): + # wait for a while + if sm_wait_time[tid] >= BackupSMWaitTime.LIFT_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from MID to APPR goal") + sm_state[tid] = BackupSM.APPROACH_ABOVE_GOAL + sm_wait_time[tid] = 0.0 + elif state == BackupSM.APPROACH_ABOVE_GOAL: + #print("[SM] Approach above goal") + offset_pos = wp.transform_get_translation(offset[tid]) + offset_rot = wp.transform_get_rotation(offset[tid]) + offset_pos = wp.vec3(offset_pos.x, offset_pos.y, offset_pos.z) # raise 25 cm + new_offset = wp.transform(offset_pos, offset_rot) + above_target_pose = wp.transform_multiply(new_offset, final_object_pose[tid]) + # Blend time for smooth approach + APPROACH_BLEND_TIME = 0.4 # seconds, tune as needed + alpha = wp.clamp(sm_wait_time[tid] / APPROACH_BLEND_TIME, 0.0, 1.0) + # Interpolate position and rotation + current_pos = wp.transform_get_translation(ee_pose[tid]) + current_rot = wp.transform_get_rotation(ee_pose[tid]) + + target_pos = wp.transform_get_translation(above_target_pose) + target_rot = wp.transform_get_rotation(above_target_pose) + + pos_interp = wp.lerp(current_pos, target_pos, alpha) + rot_interp = wp.quat_slerp(current_rot, target_rot, alpha) + # Set interpolated pose + des_ee_pose[tid] = wp.transform(pos_interp, rot_interp) + gripper_state[tid] = GripperState.CLOSE + # Evaluate readiness to transition + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + 0.02): + if sm_wait_time[tid] >= BackupSMWaitTime.APPROACH_ABOVE_GOAL: + print("[SM_INFO] : Moving from APPR_ABOVE to APPROACH_GOAL") + sm_state[tid] = BackupSM.APPROACH_GOAL + sm_wait_time[tid] = 0.0 + elif state == BackupSM.APPROACH_GOAL: + #print("[SM] approach goal") + # print("[SM_INFO] : in state ungrasp") + pose_pos = wp.transform_get_translation(final_object_pose[tid]) + pose_rot = wp.transform_get_rotation(final_object_pose[tid]) + # Apply offset in x-direction (5 cm = 0.05 m) + offset_pos = wp.vec3(pose_pos.x, pose_pos.y+0.1, pose_pos.z+0.02) + des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + 0.01, + ): + if sm_wait_time[tid] >= BackupSMWaitTime.APPROACH_GOAL: + # move to next state and reset wait time + print("[SM_INFO] : Moving from apprach goal to rest") + sm_state[tid] = BackupSM.REST + sm_wait_time[tid] = 0.0 + + + # increment wait time + sm_wait_time[tid] = sm_wait_time[tid] + dt[tid] + debug_des_pose[tid] = des_ee_pose[tid] + debug_cur_pose[tid] = ee_pose[tid] + debug_state[tid] = sm_state[tid] + + + +class BackupControllerInsertSM: + def __init__(self, dt: float, num_envs: int, device: torch.device | str = "cpu", position_threshold=0.02, offset = torch.tensor([[-0.1, 0, 0.1]], device='cuda:0')): + """Initialize the state machine. + + Args: + dt: The environment time step. + num_envs: The number of environments to simulate. + device: The device to run the state machine on. + """ + ## debug + + print(f'dt : {dt},num_envs {num_envs}, device {device}, pos_thresh {position_threshold} ') + # save parameters + self.dt = float(dt) + self.num_envs = num_envs + self.device = device + self.position_threshold = position_threshold + + # initialize state machine + self.sm_dt = torch.full((self.num_envs,), self.dt, device=self.device) + self.sm_state = torch.full((self.num_envs,), 0, dtype=torch.int32, device=self.device) + self.sm_wait_time = torch.zeros((self.num_envs,), device=self.device) + + # desired state + self.des_ee_pose = torch.zeros((self.num_envs, 7), device=self.device) + self.des_gripper_state = torch.full((self.num_envs,), 0.0, device=self.device) + self.final_object_pose = torch.zeros((self.num_envs, 7), device=self.device) + self.rest_pose = torch.zeros((self.num_envs, 7), device=self.device) + # approach above object offset + + self.offset = torch.zeros((self.num_envs, 7), device=self.device) + self.offset[:, 2] = 0.2 + self.offset[:,0] = -0.1 + self.offset[:, -1] = 1.0 # warp expects quaternion as (x, y, z, w) + + # convert to warp + self.sm_dt_wp = wp.from_torch(self.sm_dt, wp.float32) + self.sm_state_wp = wp.from_torch(self.sm_state, wp.int32) + self.sm_wait_time_wp = wp.from_torch(self.sm_wait_time, wp.float32) + self.des_ee_pose_wp = wp.from_torch(self.des_ee_pose, wp.transform) + self.des_gripper_state_wp = wp.from_torch(self.des_gripper_state, wp.float32) + self.final_object_pose_wp = wp.from_torch(self.final_object_pose, wp.transform) + self.rest_pose_wp = wp.from_torch(self.rest_pose, wp.transform) + self.offset_wp = wp.from_torch(self.offset, wp.transform) + ## For Debug + self.debug_des_pose = torch.zeros((self.num_envs, 7), device=self.device) + self.debug_cur_pose = torch.zeros((self.num_envs, 7), device=self.device) + self.debug_state = torch.full((self.num_envs,), 0, dtype=torch.int32, device=self.device) + + self.debug_des_pose_wp = wp.from_torch(self.debug_des_pose, wp.transform) + self.debug_cur_pose_wp = wp.from_torch(self.debug_cur_pose, wp.transform) + self.debug_state_wp = wp.from_torch(self.debug_state, wp.int32) + + def reset_idx(self, env_ids: Sequence[int] = None): + """Reset the state machine.""" + print(f'[SM RESET]') + if env_ids is None: + env_ids = slice(None) + self.sm_state[env_ids] = 0 + self.sm_wait_time[env_ids] = 0.0 + + def _ensure_7d(self,tensor: torch.Tensor, device): + # accepts (N,3) -> returns (N,7) with identity quaternion + if tensor.ndim != 2: + raise ValueError(f"expected 2D tensor, got shape {tensor.shape}") + if tensor.shape[1] == 7: + return tensor + if tensor.shape[1] == 3: + quat = torch.tensor([0.0,0.0,0.0,1.0], device=device, dtype=tensor.dtype) + quat = quat.unsqueeze(0).expand(tensor.shape[0], -1) + return torch.cat([tensor, quat], dim=-1) + raise ValueError(f"pose must have 3 or 7 elements, got {tensor.shape[1]}") + + + def compute(self, ee_pose: torch.Tensor, start_object_pose: torch.Tensor, current_object_pose: torch.Tensor, final_object_pose: torch.Tensor, rest_pose: torch.Tensor, sm_state: int =0, env_num = 0) -> torch.Tensor: + """Compute the desired state of the robot's end-effector and the gripper.""" + # convert all transformations from (w, x, y, z) to (x, y, z, w) + # print(f'[SM DEBUG] Doing State Comp') + # print(f'[SM DEBUG] ee_pose {ee_pose}') + # print(f'[SM DEBUG] object pose {object_pose}') + # print(f'[SM DEBUG] desired pose {des_object_pose}') + # print(f'[SM DEBUG] final pose {final_object_pose}') + # print(f'[SM DEBUG] rest pose {rest_pose}') + # before reordering + # print("\n[BackupControllerSM::compute] called with inputs:") + # print(f" sm_state: {sm_state}, env_num: {env_num}") + + # def short_tensor(t, name): + # if t is None: + # print(f" {name}: None") + # elif not isinstance(t, torch.Tensor): + # print(f" {name}: {t} (type={type(t)})") + # else: + # shape = tuple(t.shape) + # device = t.device + # dtype = t.dtype + # # show first few elements flattened + # vals = t.flatten()[:6].detach().cpu().numpy() + # print(f" {name}: shape={shape}, device={device}, dtype={dtype}, values={vals}") + + # short_tensor(ee_pose, "ee_pose") + # short_tensor(object_pose, "object_pose") + # short_tensor(des_object_pose, "des_object_pose") + # short_tensor(final_object_pose, "final_object_pose") + # short_tensor(rest_pose, "rest_pose") + # print("-" * 80) + # print(f"Got given state {sm_state}") + current_object_pose = self._ensure_7d(current_object_pose, self.device) + final_object_pose = self._ensure_7d(final_object_pose, self.device) + ee_pose = self._ensure_7d(ee_pose, self.device) + start_object_pose = self._ensure_7d(start_object_pose, self.device) + rest_pose = self._ensure_7d(rest_pose, self.device) + # now safe to reorder + ee_pose = ee_pose[:, [0, 1, 2, 4, 5, 6, 3]] + start_object_pose = start_object_pose[:, [0, 1, 2, 4, 5, 6, 3]] + current_object_pose = current_object_pose[:, [0, 1, 2, 4, 5, 6, 3]] + final_object_pose = final_object_pose[:, [0, 1, 2, 4, 5, 6, 3]] + rest_pose = rest_pose[:, [0, 1, 2, 4, 5, 6, 3]] + # convert to warp + ee_pose_wp = wp.from_torch(ee_pose.contiguous(), wp.transform) + start_object_pose_wp = wp.from_torch(start_object_pose.contiguous(), wp.transform) + current_object_pose_wp = wp.from_torch(current_object_pose.contiguous(), wp.transform) + final_object_pose_wp = wp.from_torch(final_object_pose.contiguous(), wp.transform) + rest_pose_wp = wp.from_torch(rest_pose.contiguous(), wp.transform) + # run state machine + # get state from env - convert to warp again + self.sm_state[env_num] = sm_state + self.sm_state_wp = wp.from_torch(self.sm_state, wp.int32) + # launch + wp.launch( + kernel=infer_state_machine, + dim=self.num_envs, + inputs=[ + self.sm_dt_wp, + self.sm_state_wp, + self.sm_wait_time_wp, + ee_pose_wp, + start_object_pose_wp, + current_object_pose_wp, + final_object_pose_wp, + rest_pose_wp, + self.des_ee_pose_wp, + self.des_gripper_state_wp, + self.offset_wp, + self.position_threshold, + self.debug_des_pose_wp, + self.debug_cur_pose_wp, + self.debug_state_wp, + ], + device=self.device, + ) + des_debug = self.debug_des_pose.detach().cpu().numpy() + cur_debug = self.debug_cur_pose.detach().cpu().numpy() + debug_state = self.debug_state.detach().cpu().numpy() + for i in range(self.num_envs): + des_pos, des_quat = des_debug[i][:3], des_debug[i][3:] + cur_pos, cur_quat = cur_debug[i][:3], cur_debug[i][3:] + # compute Euclidean distance with numpy + dist = np.linalg.norm(cur_pos - des_pos) + # print(f"StateMachine debug state {debug_state}") + # print(f"[Env {i}]") + # print(f" Current -> pos: {cur_pos}, quat: {cur_quat}") + # print(f" Desired -> pos: {des_pos}, quat: {des_quat}") + #print(f" Euclidean distance: {dist:.4f}") + #print(f"returned state : {self.sm_state}") + # convert transformations back to (w, x, y, z) + des_ee_pose = self.des_ee_pose[:, [0, 1, 2, 6, 3, 4, 5]] + # convert to torch + #print(f'[SM DEBUG] new desired poition : {des_ee_pose}, gripper state {self.des_gripper_state}') + return des_ee_pose , self.des_gripper_state, self.sm_state.clone().item() + #return torch.cat([des_ee_pose, self.des_gripper_state.unsqueeze(-1)], dim=-1) \ No newline at end of file diff --git a/scripts/imitation_learning/robomimic/backup_controller_place.py b/scripts/imitation_learning/robomimic/backup_controller_place.py new file mode 100644 index 000000000000..e68256b52eff --- /dev/null +++ b/scripts/imitation_learning/robomimic/backup_controller_place.py @@ -0,0 +1,456 @@ +import warp as wp +import gymnasium as gym +import torch +from collections.abc import Sequence +import numpy as np + +# this is a backup state machine controller +# initialize warp +wp.init() + + +class GripperState: + """States for the gripper.""" + + OPEN = wp.constant(1.0) + CLOSE = wp.constant(-1.0) + +class BackupSMWaitTime: + """Additional wait times (in s) for states for before switching.""" + + REST = wp.constant(0.5) + APPROACH_ABOVE_OBJECT = wp.constant(0.3) + APPROACH_OBJECT = wp.constant(0.3) + GRASP_OBJECT = wp.constant(0.1) # 1 second wait + 0.3 for gripper to close + LIFT_OBJECT = wp.constant(0.3) + MIDPOINT = wp.constant(0.1) + APPROACH_ABOVE_GOAL = wp.constant(0.1) + APPROACH_GOAL = wp.constant(0.1) + UNGRASP_OBJECT = wp.constant(1.0) + +class BackupSM: + """States for the pick state machine.""" + + REST = wp.constant(0) + APPROACH_ABOVE_OBJECT = wp.constant(1) + APPROACH_OBJECT = wp.constant(2) + GRASP_OBJECT = wp.constant(3) + LIFT_OBJECT = wp.constant(4) + MIDPOINT = wp.constant(5) + APPROACH_ABOVE_GOAL = wp.constant(6) + APPROACH_GOAL = wp.constant(7) + UNGRASP_OBJECT = wp.constant(8) + +@wp.func +def distance_below_threshold(current_pos: wp.vec3, desired_pos: wp.vec3, threshold: float) -> bool: + return wp.length(current_pos - desired_pos) < threshold + + +@wp.kernel +def infer_state_machine( + dt: wp.array(dtype=float), + sm_state: wp.array(dtype=int), + sm_wait_time: wp.array(dtype=float), + ee_pose: wp.array(dtype=wp.transform), # current ee pose + object_pose: wp.array(dtype=wp.transform), # start object location + current_object_pose: wp.array(dtype=wp.transform), # current object pose + final_object_pose: wp.array(dtype=wp.transform), + rest_ee_pose : wp.array(dtype=wp.transform), # rest position + goal_quat : wp.array(dtype=wp.transform), # goal rotation + des_ee_pose: wp.array(dtype=wp.transform), # what we return + gripper_state: wp.array(dtype=float), + offset: wp.array(dtype=wp.transform), + position_threshold: float, + debug_des_pose: wp.array(dtype=wp.transform), # NEW + debug_cur_pose: wp.array(dtype=wp.transform), # NEW + debug_state: wp.array(dtype=int), # New +): + # retrieve thread id + tid = wp.tid() + # retrieve state machine state + state = sm_state[tid] + # decide next state + if state == BackupSM.REST: + # print("[SM State REST]") + # First lift up to a safe height, then go to rest position + current_ee_pos = wp.transform_get_translation(ee_pose[tid]) + rest_pos = wp.transform_get_translation(rest_ee_pose[tid]) + rest_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + + # If we're below safe height, first move up + safe_height = 0.15 # Safe z-height to avoid collisions + if current_ee_pos.z < safe_height: + # Lift straight up first + lift_pos = wp.vec3(current_ee_pos.x, current_ee_pos.y, safe_height) + des_ee_pose[tid] = wp.transform(lift_pos, rest_rot) + #print("[SM REST doing safe ee lift]") + else: + # Already at safe height, move to rest position + des_ee_pose[tid] = rest_ee_pose[tid] + + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(rest_ee_pose[tid]), + 0.02, + ): + if sm_wait_time[tid] >= BackupSMWaitTime.REST: + print("[SM_INFO] : Moving from REST to APPROACH_ABOVE_OBJECT") + # move to next state and reset wait time + sm_state[tid] = BackupSM.APPROACH_ABOVE_OBJECT + sm_wait_time[tid] = 0.0 + else: + print("[SM REST] wait timer ") + elif state == BackupSM.APPROACH_ABOVE_OBJECT: + # print("[SM] APPR above obj") + pose_pos = wp.transform_get_translation(current_object_pose[tid]) + pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + # Go to a safe height ABOVE the object (z + 0.15m) with x offset + safe_above_pos = wp.vec3(pose_pos.x - 0.1, pose_pos.y, pose_pos.z + 0.15) + des_ee_pose[tid] = wp.transform(safe_above_pos, pose_rot) + gripper_state[tid] = GripperState.OPEN # Open gripper in preparation + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + position_threshold, + ): + # wait for a while + if sm_wait_time[tid] >= BackupSMWaitTime.APPROACH_ABOVE_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from APPR_ABOVE to APPROACH_OBJECT") + sm_state[tid] = BackupSM.APPROACH_OBJECT + sm_wait_time[tid] = 0.0 + elif state == BackupSM.APPROACH_OBJECT: + # print("[SM] approach object") + + pose_pos = wp.transform_get_translation(current_object_pose[tid]) + pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + # Apply offset in x-direction (5 cm = 0.05 m) + offset_pos = wp.vec3(pose_pos.x -0.1, pose_pos.y+0.02, pose_pos.z+0.02) #sample vial + 0.04 + des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) + gripper_state[tid] = GripperState.OPEN + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + position_threshold, + ): + if sm_wait_time[tid] >= BackupSMWaitTime.APPROACH_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from APPR_OBJ to GRASP_OBJECT") + sm_state[tid] = BackupSM.GRASP_OBJECT + sm_wait_time[tid] = 0.0 + elif state == BackupSM.GRASP_OBJECT: + # print("[SM] GRASP") + pose_pos = wp.transform_get_translation(current_object_pose[tid]) + pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + # Stay at the approach position while closing gripper + offset_pos = wp.vec3(pose_pos.x - 0.1, pose_pos.y + 0.02, pose_pos.z + 0.02) + des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if sm_wait_time[tid] >= BackupSMWaitTime.GRASP_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from GRSP to LIFT_OBJECT") + sm_state[tid] = BackupSM.LIFT_OBJECT + sm_wait_time[tid] = 0.0 + + elif state == BackupSM.LIFT_OBJECT: + #print("[SM] LIFT") # uses starting position + pose_pos = wp.transform_get_translation(object_pose[tid]) + pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + # Apply offset in x-direction (5 cm = 0.05 m) + offset_pos = wp.vec3(pose_pos.x -0.1, pose_pos.y+0.02, pose_pos.z+0.2) #sample vial + 0.04 + des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + 0.03, + ): + # wait for a while + if sm_wait_time[tid] >= BackupSMWaitTime.LIFT_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from LIFT to MIDPOINT") + sm_state[tid] = BackupSM.MIDPOINT + sm_wait_time[tid] = 0.0 + elif state == BackupSM.MIDPOINT: + #print("[SM] MIDPOINT") + pose_pos = wp.transform_get_translation(rest_ee_pose[tid]) + pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + # Apply offset in x-direction (5 cm = 0.05 m) + #offset_pos = wp.vec3(pose_pos.x -0.1, pose_pos.y+0.02, pose_pos.z+0.2) #sample vial + 0.04 + des_ee_pose[tid] = rest_ee_pose[tid] + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + 0.03, + ): + # wait for a while + if sm_wait_time[tid] >= BackupSMWaitTime.LIFT_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from MID to APPR goal") + sm_state[tid] = BackupSM.APPROACH_ABOVE_GOAL + sm_wait_time[tid] = 0.0 + elif state == BackupSM.APPROACH_ABOVE_GOAL: + #print("[SM] Approach above goal") + offset_pos = wp.transform_get_translation(final_object_pose[tid]) + offset_rot = wp.transform_get_rotation(goal_quat[tid]) + safe_above_pos = wp.vec3(offset_pos.x, offset_pos.y + 0.1, offset_pos.z + 0.25) + #offset_pos = wp.vec3(offset_pos.x, offset_pos.y, offset_pos.z) # raise 25 cm + above_target_pose = wp.transform(safe_above_pos, offset_rot) + #above_target_pose = wp.transform_multiply(new_offset, final_object_pose[tid]) + # Blend time for smooth approach + # APPROACH_BLEND_TIME = 0.4 # seconds, tune as needed + # alpha = wp.clamp(sm_wait_time[tid] / APPROACH_BLEND_TIME, 0.0, 1.0) + # # Interpolate position and rotation + # current_pos = wp.transform_get_translation(ee_pose[tid]) + # current_rot = wp.transform_get_rotation(ee_pose[tid]) + + # target_pos = wp.transform_get_translation(above_target_pose) + # target_rot = wp.transform_get_rotation(above_target_pose) + + # pos_interp = wp.lerp(current_pos, target_pos, alpha) + # rot_interp = wp.quat_slerp(current_rot, target_rot, alpha) + # # Set interpolated pose + # des_ee_pose[tid] = wp.transform(pos_interp, rot_interp) + #safe_above_pos = wp.vec3(pose_pos.x - 0.1, pose_pos.y, pose_pos.z + 0.15) + des_ee_pose[tid] = above_target_pose + gripper_state[tid] = GripperState.CLOSE + # Evaluate readiness to transition + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + 0.02): + if sm_wait_time[tid] >= BackupSMWaitTime.APPROACH_ABOVE_GOAL: + print("[SM_INFO] : Moving from APPR_ABOVE to APPROACH_GOAL") + sm_state[tid] = BackupSM.APPROACH_GOAL + sm_wait_time[tid] = 0.0 + elif state == BackupSM.APPROACH_GOAL: + #print("[SM] approach goal") + # print("[SM_INFO] : in state ungrasp") + pose_pos = wp.transform_get_translation(final_object_pose[tid]) + pose_rot = wp.transform_get_rotation(goal_quat[tid]) + # Apply offset in x-direction (5 cm = 0.05 m) + offset_pos = wp.vec3(pose_pos.x, pose_pos.y+0.1, pose_pos.z+0.1) + des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + 0.02, + ): + if sm_wait_time[tid] >= BackupSMWaitTime.APPROACH_GOAL: + # move to next state and reset wait time + print("[SM_INFO] : Moving from approach goal to ungrasp") + sm_state[tid] = BackupSM.UNGRASP_OBJECT + sm_wait_time[tid] = 0.0 + elif state == BackupSM.UNGRASP_OBJECT: + # print("[SM] GRASP") + pose_pos = wp.transform_get_translation(ee_pose[tid]) + pose_rot = wp.transform_get_rotation(goal_quat[tid]) + # Stay at the approach position while closing gripper + offset_pos = wp.vec3(pose_pos.x , pose_pos.y + 0.15, pose_pos.z) + des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) + gripper_state[tid] = GripperState.OPEN + # wait for a while + if sm_wait_time[tid] >= BackupSMWaitTime.UNGRASP_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from UNGRASP_OBJECT to REST") + sm_state[tid] = BackupSM.REST + sm_wait_time[tid] = 0.0 + + # increment wait time + sm_wait_time[tid] = sm_wait_time[tid] + dt[tid] + debug_des_pose[tid] = des_ee_pose[tid] + debug_cur_pose[tid] = ee_pose[tid] + debug_state[tid] = sm_state[tid] + + + +class BackupControllerPlaceSM: + def __init__(self, dt: float, num_envs: int, device: torch.device | str = "cpu", position_threshold=0.02, offset = torch.tensor([[-0.1, 0, 0.1]], device='cuda:0'), goal_quat = torch.tensor([[0, 0, 0, 0,0,0,0]], device='cuda:0'), goal_pos = torch.tensor([[0, 0, 0, 0,0,0,0]], device='cuda:0')): + """Initialize the state machine. + + Args: + dt: The environment time step. + num_envs: The number of environments to simulate. + device: The device to run the state machine on. + """ + ## debug + + print(f'dt : {dt},num_envs {num_envs}, \n device {device}, pos_thresh {position_threshold} \n goal_quat {goal_quat} \n goal_pos {goal_pos}') + # save parameters + self.dt = float(dt) + self.num_envs = num_envs + self.device = device + self.position_threshold = position_threshold + self.goal_quat = goal_quat + self.goal_pos = goal_pos + # initialize state machine + self.sm_dt = torch.full((self.num_envs,), self.dt, device=self.device) + self.sm_state = torch.full((self.num_envs,), 0, dtype=torch.int32, device=self.device) + self.sm_wait_time = torch.zeros((self.num_envs,), device=self.device) + + # desired state + self.des_ee_pose = torch.zeros((self.num_envs, 7), device=self.device) + self.des_gripper_state = torch.full((self.num_envs,), 0.0, device=self.device) + self.final_object_pose = torch.zeros((self.num_envs, 7), device=self.device) + self.rest_pose = torch.zeros((self.num_envs, 7), device=self.device) + + # approach above object offset + + self.offset = torch.zeros((self.num_envs, 7), device=self.device) + self.offset[:, 2] = 0.2 + self.offset[:,0] = -0.1 + self.offset[:, -1] = 1.0 # warp expects quaternion as (x, y, z, w) + + # convert to warp + self.sm_dt_wp = wp.from_torch(self.sm_dt, wp.float32) + self.sm_state_wp = wp.from_torch(self.sm_state, wp.int32) + self.sm_wait_time_wp = wp.from_torch(self.sm_wait_time, wp.float32) + self.des_ee_pose_wp = wp.from_torch(self.des_ee_pose, wp.transform) + self.des_gripper_state_wp = wp.from_torch(self.des_gripper_state, wp.float32) + self.final_object_pose_wp = wp.from_torch(self.final_object_pose, wp.transform) + self.rest_pose_wp = wp.from_torch(self.rest_pose, wp.transform) + self.offset_wp = wp.from_torch(self.offset, wp.transform) + self.goal_quat_wp = wp.from_torch(self.goal_quat, wp.transform) + ## For Debug + self.debug_des_pose = torch.zeros((self.num_envs, 7), device=self.device) + self.debug_cur_pose = torch.zeros((self.num_envs, 7), device=self.device) + self.debug_state = torch.full((self.num_envs,), 0, dtype=torch.int32, device=self.device) + + self.debug_des_pose_wp = wp.from_torch(self.debug_des_pose, wp.transform) + self.debug_cur_pose_wp = wp.from_torch(self.debug_cur_pose, wp.transform) + self.debug_state_wp = wp.from_torch(self.debug_state, wp.int32) + + def reset_idx(self, env_ids: Sequence[int] = None): + """Reset the state machine.""" + print(f'[SM RESET]') + if env_ids is None: + env_ids = slice(None) + self.sm_state[env_ids] = 0 + self.sm_wait_time[env_ids] = 0.0 + + def _ensure_7d(self,tensor: torch.Tensor, device): + # accepts (N,3) -> returns (N,7) with identity quaternion + if tensor.ndim != 2: + raise ValueError(f"expected 2D tensor, got shape {tensor.shape}") + if tensor.shape[1] == 7: + return tensor + if tensor.shape[1] == 3: + quat = torch.tensor([0.0,0.0,0.0,1.0], device=device, dtype=tensor.dtype) + quat = quat.unsqueeze(0).expand(tensor.shape[0], -1) + return torch.cat([tensor, quat], dim=-1) + raise ValueError(f"pose must have 3 or 7 elements, got {tensor.shape[1]}") + + + def compute(self, ee_pose: torch.Tensor, start_object_pose: torch.Tensor, current_object_pose: torch.Tensor, final_object_pose: torch.Tensor, rest_pose: torch.Tensor, sm_state: int =0, env_num = 0) -> torch.Tensor: + """Compute the desired state of the robot's end-effector and the gripper.""" + # convert all transformations from (w, x, y, z) to (x, y, z, w) + # print(f'[SM DEBUG] Doing State Comp') + # print(f'[SM DEBUG] ee_pose {ee_pose}') + # print(f'[SM DEBUG] object pose {object_pose}') + # print(f'[SM DEBUG] desired pose {des_object_pose}') + # print(f'[SM DEBUG] final pose {final_object_pose}') + # print(f'[SM DEBUG] rest pose {rest_pose}') + # before reordering + # print("\n[BackupControllerSM::compute] called with inputs:") + # print(f" sm_state: {sm_state}, env_num: {env_num}") + + # def short_tensor(t, name): + # if t is None: + # print(f" {name}: None") + # elif not isinstance(t, torch.Tensor): + # print(f" {name}: {t} (type={type(t)})") + # else: + # shape = tuple(t.shape) + # device = t.device + # dtype = t.dtype + # # show first few elements flattened + # vals = t.flatten()[:6].detach().cpu().numpy() + # print(f" {name}: shape={shape}, device={device}, dtype={dtype}, values={vals}") + + # short_tensor(ee_pose, "ee_pose") + # short_tensor(object_pose, "object_pose") + # short_tensor(des_object_pose, "des_object_pose") + # short_tensor(final_object_pose, "final_object_pose") + # short_tensor(rest_pose, "rest_pose") + # print("-" * 80) + # print(f"Got given state {sm_state}") + current_object_pose = self._ensure_7d(current_object_pose, self.device) + final_object_pose = self._ensure_7d(self.goal_pos, self.device) + ee_pose = self._ensure_7d(ee_pose, self.device) + start_object_pose = self._ensure_7d(start_object_pose, self.device) + rest_pose = self._ensure_7d(rest_pose, self.device) + goal_quat = self._ensure_7d(self.goal_quat, self.device) + # now safe to reorder + ee_pose = ee_pose[:, [0, 1, 2, 4, 5, 6, 3]] + start_object_pose = start_object_pose[:, [0, 1, 2, 4, 5, 6, 3]] + current_object_pose = current_object_pose[:, [0, 1, 2, 4, 5, 6, 3]] + final_object_pose = final_object_pose[:, [0, 1, 2, 4, 5, 6, 3]] + rest_pose = rest_pose[:, [0, 1, 2, 4, 5, 6, 3]] + goal_quat = goal_quat[:, [0, 1, 2, 4, 5, 6, 3]] + # convert to warp + ee_pose_wp = wp.from_torch(ee_pose.contiguous(), wp.transform) + start_object_pose_wp = wp.from_torch(start_object_pose.contiguous(), wp.transform) + current_object_pose_wp = wp.from_torch(current_object_pose.contiguous(), wp.transform) + final_object_pose_wp = wp.from_torch(final_object_pose.contiguous(), wp.transform) + rest_pose_wp = wp.from_torch(rest_pose.contiguous(), wp.transform) + goal_quat_wp = wp.from_torch(goal_quat.contiguous(), wp.transform) + # run state machine + # get state from env - convert to warp again + self.sm_state[env_num] = sm_state + self.sm_state_wp = wp.from_torch(self.sm_state, wp.int32) + # launch + wp.launch( + kernel=infer_state_machine, + dim=self.num_envs, + inputs=[ + self.sm_dt_wp, + self.sm_state_wp, + self.sm_wait_time_wp, + ee_pose_wp, + start_object_pose_wp, + current_object_pose_wp, + final_object_pose_wp, + rest_pose_wp, + goal_quat_wp, + self.des_ee_pose_wp, + self.des_gripper_state_wp, + self.offset_wp, + self.position_threshold, + self.debug_des_pose_wp, + self.debug_cur_pose_wp, + self.debug_state_wp, + ], + device=self.device, + ) + des_debug = self.debug_des_pose.detach().cpu().numpy() + cur_debug = self.debug_cur_pose.detach().cpu().numpy() + debug_state = self.debug_state.detach().cpu().numpy() + goal_quat_debug = self.goal_quat.detach().cpu().numpy() + for i in range(self.num_envs): + des_pos, des_quat = des_debug[i][:3], des_debug[i][3:] + cur_pos, cur_quat = cur_debug[i][:3], cur_debug[i][3:] + goal_quat = goal_quat_debug[i][3:] + # compute Euclidean distance with numpy + dist = np.linalg.norm(cur_pos - des_pos) + # print(f"StateMachine debug state {debug_state}") + # print(f"[Env {i}]") + # print(f" Goal pose : {final_object_pose}") + # print(f" Current -> pos: {cur_pos}, quat: {cur_quat}") + # print(f" Desired -> pos: {des_pos}, quat: {des_quat}") + # print(f" Goal quat -> : {goal_quat}") + # print(f" Euclidean distance: {dist:.4f}") + #print(f"returned state : {self.sm_state}") + # convert transformations back to (w, x, y, z) + des_ee_pose = self.des_ee_pose[:, [0, 1, 2, 6, 3, 4, 5]] + # convert to torch + #print(f'[SM DEBUG] new desired poition : {des_ee_pose}, gripper state {self.des_gripper_state}') + return des_ee_pose , self.des_gripper_state, self.sm_state.clone().item() + #return torch.cat([des_ee_pose, self.des_gripper_state.unsqueeze(-1)], dim=-1) \ No newline at end of file diff --git a/scripts/imitation_learning/robomimic/evaluation.py b/scripts/imitation_learning/robomimic/evaluation.py index ec2ff30d7338..25eaf82d4d88 100644 --- a/scripts/imitation_learning/robomimic/evaluation.py +++ b/scripts/imitation_learning/robomimic/evaluation.py @@ -42,7 +42,10 @@ def ensemble_uncertainty(ensemble, obs): max_action = torch.max(outputs, dim=0) std = torch.std(outputs, dim=0) + variance = std.pow(2) + #print(f"ens variance : {variance}") + diff = 0 # std per policy (overall std across joints for each policy's action) per_policy_std = torch.std(outputs, dim=1) @@ -57,7 +60,8 @@ def ensemble_uncertainty(ensemble, obs): 'std': std, 'variance': variance, 'min_std_action': min_std_action, - 'time_taken': time_taken + 'time_taken': time_taken, + 'diff': diff } def MC_dropout_uncertainty(policy, obs, niters=50): diff --git a/scripts/imitation_learning/robomimic/play.py b/scripts/imitation_learning/robomimic/play.py index 02d293b21eba..18a973d3f866 100644 --- a/scripts/imitation_learning/robomimic/play.py +++ b/scripts/imitation_learning/robomimic/play.py @@ -89,6 +89,8 @@ def rollout(policy, env, success_term, horizon, device): """ policy.start_episode() obs_dict, _ = env.reset() + print("[INFO] Policy expected obs keys:", policy) + traj = dict(actions=[], obs=[], next_obs=[]) for i in range(horizon): diff --git a/scripts/imitation_learning/robomimic/play_ensemble.py b/scripts/imitation_learning/robomimic/play_ensemble.py index a7ea7565e273..b2062ac57c87 100644 --- a/scripts/imitation_learning/robomimic/play_ensemble.py +++ b/scripts/imitation_learning/robomimic/play_ensemble.py @@ -246,7 +246,7 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, certain_joint_positions = [] ###### SET UP RECOVERY #### - use_recovery=True + use_recovery=False recovery_activated_during_rollout = 0 print("rollout recovery enabled ? : ", use_recovery) @@ -259,7 +259,7 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, max_recovery_steps = 500 # Safety limit for recovery mode ##### CONFIG RECOVERY CONTROLLER #### - backup_controller = BackupController(env, device) + backup_controller = BackupController(env, device, ) state_guess = 0 last_state = 0 recovery_mode = False diff --git a/scripts/imitation_learning/robomimic/play_ensemble_v05.py b/scripts/imitation_learning/robomimic/play_ensemble_v05.py index 43771bc8d2ac..c10cfccf1be6 100644 --- a/scripts/imitation_learning/robomimic/play_ensemble_v05.py +++ b/scripts/imitation_learning/robomimic/play_ensemble_v05.py @@ -120,7 +120,7 @@ def policy_from_checkpoint_v05(ckpt_path, device, verbose=True): from packaging import version # Check robomimic version - robomimic_version = getattr(robomimic, '__version__', '0.3.0') + robomimic_version = getattr(robomimic, '__version__', '0.5.0') if verbose: print(f"[INFO] Loading checkpoint with robomimic version {robomimic_version}") @@ -324,7 +324,7 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, obs_dict, _ = env.reset() traj = dict(actions=[], obs=[], next_obs=[], sub_obs=[], uncertainties=[], min_actions=[], max_actions=[], - time_taken=[], joint_pos=[], recovery=[], failure=[], grasp=[], appr=[]) + time_taken=[], joint_pos=[], recovery=[], failure=[], grasp=[], appr=[], diff=[]) ###### SETUP SWITCHING LOGIC #### switchingLogic = SwitchingLogic(parameters, horizon=horizon) @@ -344,7 +344,7 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, max_recovery_steps = 500 # Safety limit for recovery mode ##### CONFIG RECOVERY CONTROLLER #### - backup_controller = BackupController(env, device) + backup_controller = BackupController(env, device, tasktype="lift") state_guess = 0 last_state = 0 recovery_mode = False @@ -386,24 +386,12 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, # This prevents dimension mismatch errors when env has extra observations policy_obs = {k: obs[k] for k in TRAINED_OBS_KEYS if k in obs} - # Debug: print observation shapes on first iteration - if i == 0: - print(f"[DEBUG] Available obs keys: {list(obs.keys())}") - print(f"[DEBUG] Filtered policy_obs keys: {list(policy_obs.keys())}") - total_dim = 0 - for k, v in policy_obs.items(): - dim = v.shape[-1] if len(v.shape) > 0 else 1 - print(f"[DEBUG] {k}: shape={v.shape}, dim={dim}") - total_dim += dim - print(f"[DEBUG] Total dimension: {total_dim} (expected 44)") - missing = [k for k in TRAINED_OBS_KEYS if k not in obs] - if missing: - print(f"[DEBUG] MISSING keys: {missing}") - #### get policy action #### metrics = ensemble_uncertainty(ensemble, policy_obs) uncertainty = metrics['variance'] + #print(f"Uncdertainty : {uncertainty}") policy_actions = metrics['mean'] + diff=metrics['diff'] # Robust state estimation based on actual observations is_grasping = bool(sub_obs['grasp'].item()) if sub_obs['grasp'].numel() == 1 else bool(torch.any(sub_obs['grasp'])) @@ -419,7 +407,8 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, if state_guess >= 4 and state_guess < 7: # Was in lift/move states # Object dropped - need to re-approach state_guess = 0 # Go back to REST to safely re-approach - print(f"[STATE] Reset to REST (grasp lost)") + #print(f"[STATE] Reset to REST (grasp lost), activating recovery") + # recovery_mode= True sm_actions, state_guess = backup_controller.get_controller_action(state_guess, 0) if last_state != state_guess: @@ -440,24 +429,25 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, print(f"[RECOVERY] Step {i} - Starting recovery from state {state_guess}") if recovery_mode: + #print(f"Recovery Actions") actions = sm_actions recovery_steps += 1 # Exit recovery when task completes or after max steps - max_recovery_steps = 500 - if state_guess == 7 or recovery_steps > max_recovery_steps: # APPROACH_GOAL = 7 - print(f"[RECOVERY] Completed after {recovery_steps} steps, state={state_guess}") - recovery_mode = False - recovery_cooldown_active = True - recovery_cooldown_timer = recovery_cooldown_duration + # max_recovery_steps = 500 + # if recovery_steps > max_recovery_steps: # APPROACH_GOAL = 7 + # print(f"[RECOVERY] Completed after {recovery_steps} steps, state={state_guess}") + # recovery_mode = False + # recovery_cooldown_active = True + # recovery_cooldown_timer = recovery_cooldown_duration else: # Decrement cooldown when not in recovery - if recovery_cooldown_active: - recovery_cooldown_timer -= 1 - if recovery_cooldown_timer <= 0: - recovery_cooldown_active = False - recovery_cooldown_timer = recovery_cooldown_duration - print("Recovery Cooldown Ended") + # if recovery_cooldown_active: + # recovery_cooldown_timer -= 1 + # if recovery_cooldown_timer <= 0: + # recovery_cooldown_active = False + # recovery_cooldown_timer = recovery_cooldown_duration + # print("Recovery Cooldown Ended") actions = policy_actions @@ -485,6 +475,7 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, traj['time_taken'].append(metrics['time_taken']) traj['joint_pos'].append(obs['abs_joint_pos']) traj['recovery'].append(recovery) + traj['diff'].append(diff) if torch.any(obs['object_knocked']): print("[MONITOR] object knocked over") object_knocked = True @@ -494,7 +485,8 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, traj['failure'].append(" ") grasp = torch.any(sub_obs['grasp']) traj['grasp'].append(grasp.item()) - appr = torch.any(sub_obs['stacked']) + #appr = torch.any(sub_obs['stacked']) + appr = torch.any(sub_obs['appr_goal']) traj['appr'].append(appr.item()) print(f"grasp : {traj['grasp']}, appr : {traj['appr']}") # print(f"grasp : {grasp.item()}, appr : {appr.item()}") @@ -503,7 +495,8 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, elif terminated or truncated: grasp = torch.any(sub_obs['grasp']) traj['grasp'].append(grasp.item()) - appr = torch.any(sub_obs['stacked']) + #appr = torch.any(sub_obs['stacked']) + appr = torch.any(sub_obs['appr_goal']) traj['appr'].append(appr.item()) #print(f"grasp : {traj['grasp']}, appr : {traj['appr']}") if i==horizon: @@ -519,7 +512,8 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, traj['failure'].append("timeout") grasp = torch.any(sub_obs['grasp']) traj['grasp'].append(grasp.item()) - appr = torch.any(sub_obs['stacked']) + #appr = torch.any(sub_obs['stacked']) + appr = torch.any(sub_obs['appr_goal']) traj['appr'].append(appr.item()) return False, traj, recovery_activated_during_rollout, "" @@ -599,51 +593,56 @@ def main(): # stack_cube_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/stack_cube_ensemble.txt') #pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/ensembles.txt') - pick_place_ensemble = load_ensemble(device, ensemble_path='docs/place/Dev-IK-Rel-Place-v0/best_models/best_model_paths.txt') + #pick_place_ensemble = load_ensemble(device, ensemble_path='docs/place/low/Dev-IK-Rel-Place-v0/best_models/best_model_paths.txt') # pick_place_ensemble_30 = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/pick_place_ensemble_30_paths.txt') - + # pick_place_ensemble = load_ensemble(device, ensemble_path='docs/lift/Dev-IK-Rel-v1/best_models/best_model_paths.txt') + + pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/insert_paths.txt') + # Lets set these to the 0.99 confidence parameters = { 'beaker_lift' :{ - 0 : { - "confidence_level": 0.018646507043923632, - "window_size": 15, - "max_peaks": 10 + 0 : { + "confidence_level": 0.0002104382060000009, + "window_size": 10, + "max_peaks": 8 }, 1 : { - "confidence_level": 0.018898154803458085, + "confidence_level": 0.00020459246700000051, "window_size": 10, - "max_peaks": 9 + "max_peaks": 8 }, 2 : { - "confidence_level": 0.017589774107725654, - "window_size": 15, - "max_peaks": 7 + "confidence_level": 0.00023551160300000013, + "window_size": 10, + "max_peaks": 8 }, 3 : { - "confidence_level": 0.02829341592326851, - "window_size": 12, + "confidence_level": 0.0003701742110000007, + "window_size": 10, "max_peaks": 8 }, 4 : { - "confidence_level": 0.029170220902127186, - "window_size": 15, - "max_peaks": 11 + "confidence_level": 0.0002939644620000011, + "window_size": 10, + "max_peaks": 8 }, 5 : { - "confidence_level": 0.017449474558650185, - "window_size": 12, - "max_peaks": 7 + "confidence_level": 0.0006407762280000002, + "window_size": 10, + "max_peaks": 8 }, 6 : { - "confidence_level": 0.6324478323150455, - "window_size": 14, - "max_peaks": 14 + "confidence_level": 0.49527883679, + "window_size": 10, + "max_peaks": 8 }, }, } + + model_arch = "BC_RNN_GMM" task = "pick_place" # stack_cube or pick_place model_name = f"ensemble" @@ -756,6 +755,7 @@ def main(): summary_text += f"Success rate: {results.count(True) / len(results)}\n" summary_text += f'Failed runs with no intervention : {sum(failed_no_intervention)}\n' summary_text += f"Intervention rate : {sum(intervention)/len(intervention)}. Of which success : {sum(intervention_success)}, of which failed {sum(intervention_failure)}\n" + summary_text += f"Intervention Results: {intervention}\n" summary_text += f"Number of times object knocked over : {knocked}\n" summary_text += f"Trial Results: {results}\n\n" summary_text += f"Environment random seed: {args_cli.seed}\n" diff --git a/scripts/imitation_learning/robomimic/play_with_backup.py b/scripts/imitation_learning/robomimic/play_with_backup.py index 2dda0f20fe31..ef7c15bbaf15 100644 --- a/scripts/imitation_learning/robomimic/play_with_backup.py +++ b/scripts/imitation_learning/robomimic/play_with_backup.py @@ -118,13 +118,13 @@ def rollout_ensemble(env, success_term, horizon, device): # Set up recovery controller - backup_controller = BackupController(env, device) + backup_controller = BackupController(env, device, tasktype="lift") state_guess = 0 for i in range(horizon): action, state_guess = backup_controller.get_controller_action(state_guess, 0) - print(f"state guess : {state_guess}") + # print(f"state guess : {state_guess}") zero_action = torch.zeros(1, env.action_space.shape[1], device=device) #print(f"\npassed this action {action}") diff --git a/scripts/imitation_learning/robomimic/train_ensemble.py b/scripts/imitation_learning/robomimic/train_ensemble.py index b2510eed8edb..9a9f6bb5d954 100644 --- a/scripts/imitation_learning/robomimic/train_ensemble.py +++ b/scripts/imitation_learning/robomimic/train_ensemble.py @@ -177,9 +177,9 @@ def train(config: Config, device: str, log_dirs: list[str], ckpt_dirs: list[str] env_meta = FileUtils.get_env_metadata_from_dataset(dataset_path=config.train.data) print(f"config.train.data: {config.train.data}") with config.values_unlocked(): - config.train.data = [{'path': 'docs/place/glassware_stack_demos_split_normalized.hdf5'}] + config.train.data = [{'path': 'docs/place/glassware_insert_demos_generated_split.hdf5'}] ####### HARD CODED MAKE SURE THIS GETS UPDATED ######### - config_list= [{'path': 'docs/place/glassware_stack_demos_split_normalized.hdf5'}] + config_list= [{'path': 'docs/place/glassware_insert_demos_generated_split.hdf5'}] print(f"config.train.data: {config.train.data}") # print(f"config_list: {config_list}") # if isinstance(config.train.data, str): @@ -449,7 +449,7 @@ def keep_data_percentage(trainset, percentage): return trainset -def create_bootstrap_sample(trainset, seed: int, percent: float = 0.5): +def create_bootstrap_sample(trainset, seed: int, percent: float = 0.25): """Create a random sample (without replacement) of a percentage of the training set. Args: diff --git a/scripts/tools/regen_usd_file.py b/scripts/tools/regen_usd_file.py new file mode 100644 index 000000000000..d9058e6a0564 --- /dev/null +++ b/scripts/tools/regen_usd_file.py @@ -0,0 +1,85 @@ +#!/usr/bin/env python3 +"""Script to regenerate USD files for vial rack and capped vial with proper collision meshes.""" + +from isaaclab.app import AppLauncher + +# Launch Isaac Sim +app_launcher = AppLauncher(headless=True) +simulation_app = app_launcher.app + +import os +from isaaclab.sim.converters import MeshConverter, MeshConverterCfg +from isaaclab.sim.schemas import schemas_cfg +from isaaclab.utils.dict import print_dict + +def regenerate_vial_rack(): + """Regenerate vial rack USD with convex decomposition.""" + print("=" * 80) + print("Regenerating Vial Rack USD with Convex Decomposition") + print("=" * 80) + + mesh_converter_cfg = MeshConverterCfg( + asset_path="/workspace/isaaclab/source/isaaclab_assets/isaaclab_assets/glassware/vial_rack/vial_rack.obj", + usd_dir="/workspace/isaaclab/source/isaaclab_assets/isaaclab_assets/glassware/vial_rack", + usd_file_name="vial_rack.usd", + force_usd_conversion=True, + make_instanceable=True, + mass_props=schemas_cfg.MassPropertiesCfg(mass=0.1), + rigid_props=schemas_cfg.RigidBodyPropertiesCfg(), + collision_props=schemas_cfg.CollisionPropertiesCfg(collision_enabled=True), + mesh_collision_props=schemas_cfg.ConvexDecompositionPropertiesCfg( + max_convex_hulls=64, + voxel_resolution=1000000, + hull_vertex_limit=64, + min_thickness=0.0005 + ), + translation=(0.0, 0.0, 0.0), + rotation=(1.0, 0.0, 0.0, 0.0), + scale=(1.0, 1.0, 1.0), + ) + + print("Config:") + print_dict(mesh_converter_cfg.to_dict(), nesting=0) + + mesh_converter = MeshConverter(mesh_converter_cfg) + print(f"✓ Generated: {mesh_converter.usd_path}") + print() + +def regenerate_capped_vial(): + """Regenerate capped vial USD with convex hull.""" + print("=" * 80) + print("Regenerating Capped Vial USD with Convex Hull") + print("=" * 80) + + mesh_converter_cfg = MeshConverterCfg( + asset_path="/workspace/isaaclab/source/isaaclab_assets/isaaclab_assets/glassware/vial_with_cap/vial_with_cap.obj", + usd_dir="/workspace/isaaclab/source/isaaclab_assets/isaaclab_assets/glassware/vial_with_cap", + usd_file_name="vial_with_cap.usd", + force_usd_conversion=True, + make_instanceable=True, + mass_props=schemas_cfg.MassPropertiesCfg(mass=0.01), + rigid_props=schemas_cfg.RigidBodyPropertiesCfg(), + collision_props=schemas_cfg.CollisionPropertiesCfg(collision_enabled=True), + mesh_collision_props=schemas_cfg.ConvexHullPropertiesCfg( + hull_vertex_limit=32, + min_thickness=0.0005 + ), + translation=(0.0, 0.0, 0.0), + rotation=(1.0, 0.0, 0.0, 0.0), + scale=(1.0, 1.0, 1.0), + ) + + print("Config:") + print_dict(mesh_converter_cfg.to_dict(), nesting=0) + + mesh_converter = MeshConverter(mesh_converter_cfg) + print(f"✓ Generated: {mesh_converter.usd_path}") + print() + +if __name__ == "__main__": + regenerate_vial_rack() + regenerate_capped_vial() + print("=" * 80) + print("✓ All USD files regenerated successfully!") + print("=" * 80) + simulation_app.close() diff --git a/scripts/tutorials/00_sim/create_empty.py b/scripts/tutorials/00_sim/create_empty.py index f35a0d5487f0..383e26a15aaf 100644 --- a/scripts/tutorials/00_sim/create_empty.py +++ b/scripts/tutorials/00_sim/create_empty.py @@ -17,7 +17,7 @@ import argparse -from isaaclab.app import AppLauncher +from source.isaaclab.isaaclab.app import AppLauncher # create argparser parser = argparse.ArgumentParser(description="Tutorial on creating an empty stage.") diff --git a/source/chills b/source/chills new file mode 160000 index 000000000000..5aea8c2b4b8d --- /dev/null +++ b/source/chills @@ -0,0 +1 @@ +Subproject commit 5aea8c2b4b8db01a2c015f6e673e5465a4a85a3a diff --git a/source/isaaclab/isaaclab/__init__.py b/source/isaaclab/isaaclab/__init__.py index 7bb50a0eab23..b7026ff693e5 100644 --- a/source/isaaclab/isaaclab/__init__.py +++ b/source/isaaclab/isaaclab/__init__.py @@ -16,4 +16,4 @@ """Extension metadata dictionary parsed from the extension.toml file.""" # Configure the module-level variables -__version__ = ISAACLAB_METADATA["package"]["version"] +__version__ = ISAACLAB_METADATA["package"]["version"] \ No newline at end of file diff --git a/source/isaaclab/isaaclab/envs/mdp/events.py b/source/isaaclab/isaaclab/envs/mdp/events.py index f9ffd47ba874..3d5cec572085 100644 --- a/source/isaaclab/isaaclab/envs/mdp/events.py +++ b/source/isaaclab/isaaclab/envs/mdp/events.py @@ -1217,6 +1217,7 @@ def reset_place_root_state_uniform( # remote iks plate offset positions[:,0] += 0.04 positions[:,2] -= 0.06 + goal_pose[:,2]=0.0 asset2.write_root_pose_to_sim(torch.cat([positions, orientations2], dim=-1), env_ids=env_ids) asset2.write_root_velocity_to_sim(velocities, env_ids=env_ids) asset3.write_root_pose_to_sim(torch.cat([goal_pose, orientations3], dim=-1), env_ids=env_ids) diff --git a/source/isaaclab/isaaclab/safety/switchingLogic.py b/source/isaaclab/isaaclab/safety/switchingLogic.py index 92c188d98426..ceeb131727bc 100644 --- a/source/isaaclab/isaaclab/safety/switchingLogic.py +++ b/source/isaaclab/isaaclab/safety/switchingLogic.py @@ -32,10 +32,12 @@ def halfway_check(self, step:int=0, grasp_state:bool=True): def check(self, uncertainties, step) -> bool: + counts = 0 # 1. Update windows for joint_num, unc in enumerate(uncertainties): # Check for bounds if uncertainties is smaller than self.joints self.traj[joint_num][step]=unc.item() + #print(f'Action comp {joint_num} uncertainty at step {step}: {unc.item()}') if step > self.params[joint_num]['window_size']: @@ -44,10 +46,13 @@ def check(self, uncertainties, step) -> bool: #print(f'action {joint_num}, window: {test_window}') # print(f"Action comp {joint_num}, {unc.item()} limit {self.params[joint_num]['confidence_level']} manually found peaks : {peaks}") if peaks>=self.params[joint_num]['max_peaks']: - if not self.triggered: - print(f"Uncertaity in joint {joint_num} triggered") - self.triggered =True - return True - - + counts+=1 + + + if counts >2: + if not self.triggered: + self.triggered = True + print(f"Uncertaity in joint {joint_num} triggered") + print("triggered safety condition") + return True return False \ No newline at end of file diff --git a/source/isaaclab/pyproject.toml b/source/isaaclab/pyproject.toml index d90ac3536f16..e749d7a59aa1 100644 --- a/source/isaaclab/pyproject.toml +++ b/source/isaaclab/pyproject.toml @@ -1,3 +1,3 @@ [build-system] requires = ["setuptools", "wheel", "toml"] -build-backend = "setuptools.build_meta" +build-backend = "setuptools.build_meta" \ No newline at end of file diff --git a/source/isaaclab/setup.py b/source/isaaclab/setup.py index c78f98172455..9bb944e342a8 100644 --- a/source/isaaclab/setup.py +++ b/source/isaaclab/setup.py @@ -42,21 +42,24 @@ "pytest", "pytest-mock", "junitparser", - "flatdict==4.0.1", + "flatdict==4.0.0", "flaky", + "packaging", ] -# Append Linux x86_64–only deps via PEP 508 markers -X64 = "platform_machine in 'x86_64,AMD64'" +SUPPORTED_ARCHS_ARM = "platform_machine in 'x86_64,AMD64,aarch64,arm64'" +SUPPORTED_ARCHS = "platform_machine in 'x86_64,AMD64'" INSTALL_REQUIRES += [ # required by isaaclab.isaaclab.controllers.pink_ik - f"pin-pink==3.1.0 ; platform_system == 'Linux' and ({X64})", + f"pin-pink==3.1.0 ; platform_system == 'Linux' and ({SUPPORTED_ARCHS_ARM})", + f"daqp==0.7.2 ; platform_system == 'Linux' and ({SUPPORTED_ARCHS_ARM})", # required by isaaclab.devices.openxr.retargeters.humanoid.fourier.gr1_t2_dex_retargeting_utils - f"dex-retargeting==0.4.6 ; platform_system == 'Linux' and ({X64})", + f"dex-retargeting==0.4.6 ; platform_system == 'Linux' and ({SUPPORTED_ARCHS})", ] PYTORCH_INDEX_URL = ["https://download.pytorch.org/whl/cu128"] +# Installation operation # Installation operation setup( name="isaaclab", @@ -78,6 +81,7 @@ "Programming Language :: Python :: 3.11", "Isaac Sim :: 4.5.0", "Isaac Sim :: 5.0.0", + "Isaac Sim :: 5.1.0", ], zip_safe=False, ) diff --git a/source/isaaclab_mimic/isaaclab_mimic/datagen/generation.py b/source/isaaclab_mimic/isaaclab_mimic/datagen/generation.py index 6abdc088170d..1390fdf48eaf 100644 --- a/source/isaaclab_mimic/isaaclab_mimic/datagen/generation.py +++ b/source/isaaclab_mimic/isaaclab_mimic/datagen/generation.py @@ -111,13 +111,22 @@ def env_loop( if prev_num_attempts != num_attempts: prev_num_attempts = num_attempts generated_sucess_rate = 100 * num_success / num_attempts if num_attempts > 0 else 0.0 - print("") - print("*" * 50, "\033[K") - print( - f"{num_success}/{num_attempts} ({generated_sucess_rate:.1f}%) successful demos generated by" - " mimic\033[K" - ) - print("*" * 50, "\033[K") + summary_text=f"\n" + summary_text+=f"Success Rate: {generated_sucess_rate:.1f}% ({num_success} successes, {num_failures} failures, {num_attempts} attempts)\n" + summary_text+= "*" * 50 + "\n" + summary_text+=f"{num_success}/{num_attempts} ({generated_sucess_rate:.1f}%) successful demos generated by mimic\n" + # print( + # f"{num_success}/{num_attempts} ({generated_sucess_rate:.1f}%) successful demos generated by" + # " mimic\033[K" + # ) + summary_text+="*" * 50 + "\n" + # print("*" * 50, "\033[K") + print(summary_text) + log_file = "docs/generation_log.txt" + # with open(log_file, 'a') as f: + # f.write(summary_text) + # f.write("\n" + "="*80 + "\n\n") + # termination condition is on enough successes if @guarantee_success or enough attempts otherwise generation_guarantee = env.cfg.datagen_config.generation_guarantee diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/__init__.py b/source/isaaclab_mimic/isaaclab_mimic/envs/__init__.py index c612ed504c9b..1afb3b2a6374 100644 --- a/source/isaaclab_mimic/isaaclab_mimic/envs/__init__.py +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/__init__.py @@ -5,6 +5,7 @@ """Sub-package with environment wrappers for Isaac Lab Mimic.""" +import isaaclab_mimic.envs.cube_mimic_vm_env_cfg import gymnasium as gym from .franka_bin_stack_ik_rel_mimic_env_cfg import FrankaBinStackIKRelMimicEnvCfg @@ -19,6 +20,8 @@ from .cube_blueprint_mimic_env_cfg import CubeBlueprintMimicEnvCfg from .cube_mimic_env_cfg import CubeMimicEnvCfg from .cube_mimic_env import CubeMimicEnv +from .cube_mimic_vm_env_cfg import CubeMimicVMEnvCfg +from .cube_mimic_vm_env import CubeMimicVMEnv from .cube_mimic_env_skillgen_cfg import CubeMimicEnvSkillgenCfg ## # Inverse Kinematics - Relative Pose Control @@ -198,4 +201,13 @@ "env_cfg_entry_point": cube_blueprint_mimic_env_cfg.CubeBlueprintMimicEnvCfg, }, disable_env_checker=True, +) + +gym.register( + id="Cube-Mimic-VM-v0", + entry_point="isaaclab_mimic.envs:CubeMimicVMEnv", + kwargs={ + "env_cfg_entry_point": cube_mimic_vm_env_cfg.CubeMimicVMEnvCfg, + }, + disable_env_checker=True, ) \ No newline at end of file diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env.py b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env.py index 195e1abf19ab..7324eccbb68b 100755 --- a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env.py +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env.py @@ -158,9 +158,9 @@ def get_subtask_term_signals(self, env_ids: Sequence[int] | None = None) -> dict subtask_terms = self.obs_buf["subtask_terms"] #signals["appr"] = subtask_terms["appr"][env_ids] signals["grasp"] = subtask_terms["grasp"][env_ids] - signals["stacked"] = subtask_terms["stacked"][env_ids] + #signals["stacked"] = subtask_terms["stacked"][env_ids] #signals["lift"] = subtask_terms["lift"][env_ids] - #signals["appr_goal"] = subtask_terms["appr_goal"][env_ids] + signals["appr_goal"] = subtask_terms["appr_goal"][env_ids] #signals["release_object"] = subtask_terms["release_object"][env_ids] # final subtask is placing cube on goal - signal needed when using --annotate_subtask_start_signals return signals diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py index 71ce2096b510..972daecbb642 100755 --- a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py @@ -6,8 +6,9 @@ from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig from isaaclab.utils import configclass -from isaaclab_tasks.manager_based.manipulation.cube_lift.config.franka.dev_ik_rel_env_place import FrankaDevEnvCfg - +#from isaaclab_tasks.manager_based.manipulation.cube_lift.config.franka.dev_ik_rel_env_place import FrankaDevEnvCfg +#from isaaclab_tasks.manager_based.manipulation.cube_lift.config.franka.dev_ik_rel_env_obs import FrankaDevEnvCfg +from isaaclab_tasks.manager_based.manipulation.cube_lift.config.franka.dev_ik_rel_vial_insert import FrankaDevEnvCfg @configclass class CubeMimicEnvCfg(FrankaDevEnvCfg, MimicEnvCfg): @@ -116,7 +117,7 @@ def __post_init__(self): # # This is actually the lift # subtask_term_signal="appr_goal", # # No time offsets for the final subtask - # subtask_term_offset_range=(10, 20), + # subtask_term_offset_range=(0,0), # # Selection strategy for source subtask segment # selection_strategy="nearest_neighbor_robot_distance", # # Optional parameters for the selection strategy function diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_vm_env.py b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_vm_env.py new file mode 100755 index 000000000000..78cdef0dde36 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_vm_env.py @@ -0,0 +1,189 @@ + +# Copyright (c) 2024-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +import torch +from collections.abc import Sequence + +import isaaclab.utils.math as PoseUtils +from isaaclab.envs import ManagerBasedRLMimicEnv + + +class CubeMimicVMEnv(ManagerBasedRLMimicEnv): + """ + Isaac Lab Mimic environment wrapper class for Franka Cube Stack IK Rel env. + """ + + def get_robot_eef_pose(self, eef_name: str, env_ids: Sequence[int] | None = None) -> torch.Tensor: + """ + Get current robot end effector pose. Should be the same frame as used by the robot end-effector controller. + + Args: + eef_name: Name of the end effector. + env_ids: Environment indices to get the pose for. If None, all envs are considered. + + Returns: + A torch.Tensor eef pose matrix. Shape is (len(env_ids), 4, 4) + """ + if env_ids is None: + env_ids = slice(None) + + # Retrieve end effector pose from the observation buffer + eef_pos = self.obs_buf["policy"]["eef_pos"][env_ids] + eef_quat = self.obs_buf["policy"]["eef_quat"][env_ids] + + # Quaternion format is w,x,y,z + return PoseUtils.make_pose(eef_pos, PoseUtils.matrix_from_quat(eef_quat)) + + def target_eef_pose_to_action( + self, target_eef_pose_dict: dict, gripper_action_dict: dict, noise: float | None = None, env_id: int = 0 + ) -> torch.Tensor: + """ + Takes a target pose and gripper action for the end effector controller and returns an action + (usually a normalized delta pose action) to try and achieve that target pose. + Noise is added to the target pose action if specified. + + Args: + target_eef_pose_dict: Dictionary of 4x4 target eef pose for each end-effector. + gripper_action_dict: Dictionary of gripper actions for each end-effector. + noise: Noise to add to the action. If None, no noise is added. + env_id: Environment index to get the action for. + + Returns: + An action torch.Tensor that's compatible with env.step(). + """ + eef_name = list(self.cfg.subtask_configs.keys())[0] + + # target position and rotation + (target_eef_pose,) = target_eef_pose_dict.values() + target_pos, target_rot = PoseUtils.unmake_pose(target_eef_pose) + + # current position and rotation + curr_pose = self.get_robot_eef_pose(eef_name, env_ids=[env_id])[0] + curr_pos, curr_rot = PoseUtils.unmake_pose(curr_pose) + + # normalized delta position action + delta_position = target_pos - curr_pos + + # normalized delta rotation action + delta_rot_mat = target_rot.matmul(curr_rot.transpose(-1, -2)) + delta_quat = PoseUtils.quat_from_matrix(delta_rot_mat) + delta_rotation = PoseUtils.axis_angle_from_quat(delta_quat) + + # get gripper action for single eef + (gripper_action,) = gripper_action_dict.values() + + # add noise to action + pose_action = torch.cat([delta_position, delta_rotation], dim=0) + if noise is not None: + noise = noise * torch.randn_like(pose_action) + pose_action += noise + pose_action = torch.clamp(pose_action, -1.0, 1.0) + + return torch.cat([pose_action, gripper_action], dim=0) + + def action_to_target_eef_pose(self, action: torch.Tensor) -> dict[str, torch.Tensor]: + """ + Converts action (compatible with env.step) to a target pose for the end effector controller. + Inverse of @target_eef_pose_to_action. Usually used to infer a sequence of target controller poses + from a demonstration trajectory using the recorded actions. + + Args: + action: Environment action. Shape is (num_envs, action_dim) + + Returns: + A dictionary of eef pose torch.Tensor that @action corresponds to + """ + eef_name = list(self.cfg.subtask_configs.keys())[0] + + delta_position = action[:, :3] + delta_rotation = action[:, 3:6] + + # current position and rotation + curr_pose = self.get_robot_eef_pose(eef_name, env_ids=None) + curr_pos, curr_rot = PoseUtils.unmake_pose(curr_pose) + + # get pose target + target_pos = curr_pos + delta_position + + # Convert delta_rotation to axis angle form + delta_rotation_angle = torch.linalg.norm(delta_rotation, dim=-1, keepdim=True) + delta_rotation_axis = delta_rotation / delta_rotation_angle + + # Handle invalid division for the case when delta_rotation_angle is close to zero + is_close_to_zero_angle = torch.isclose(delta_rotation_angle, torch.zeros_like(delta_rotation_angle)).squeeze(-1) + delta_rotation_axis[is_close_to_zero_angle] = torch.zeros_like(delta_rotation_axis)[is_close_to_zero_angle] + + delta_quat = PoseUtils.quat_from_angle_axis(delta_rotation_angle.squeeze(-1), delta_rotation_axis) + delta_rot_mat = PoseUtils.matrix_from_quat(delta_quat) + target_rot = torch.matmul(delta_rot_mat, curr_rot) + + target_poses = PoseUtils.make_pose(target_pos, target_rot).clone() + + return {eef_name: target_poses} + + def actions_to_gripper_actions(self, actions: torch.Tensor) -> dict[str, torch.Tensor]: + """ + Extracts the gripper actuation part from a sequence of env actions (compatible with env.step). + + Args: + actions: environment actions. The shape is (num_envs, num steps in a demo, action_dim). + + Returns: + A dictionary of torch.Tensor gripper actions. Key to each dict is an eef_name. + """ + # last dimension is gripper action + return {list(self.cfg.subtask_configs.keys())[0]: actions[:, -1:]} + + def get_subtask_term_signals(self, env_ids: Sequence[int] | None = None) -> dict[str, torch.Tensor]: + """ + Gets a dictionary of termination signal flags for each subtask in a task. The flag is 1 + when the subtask has been completed and 0 otherwise. The implementation of this method is + required if intending to enable automatic subtask term signal annotation when running the + dataset annotation tool. This method can be kept unimplemented if intending to use manual + subtask term signal annotation. + + Args: + env_ids: Environment indices to get the termination signals for. If None, all envs are considered. + + Returns: + A dictionary termination signal flags (False or True) for each subtask. + """ + if env_ids is None: + env_ids = slice(None) + + signals = dict() + subtask_terms = self.obs_buf["subtask_terms"] + #signals["appr"] = subtask_terms["appr"][env_ids] + signals["grasp"] = subtask_terms["grasp"][env_ids] + signals["stacked"] = subtask_terms["stacked"][env_ids] + #signals["lift"] = subtask_terms["lift"][env_ids] + #signals["appr_goal"] = subtask_terms["appr_goal"][env_ids] + #signals["release_object"] = subtask_terms["release_object"][env_ids] + # final subtask is placing cube on goal - signal needed when using --annotate_subtask_start_signals + return signals + + def get_expected_attached_object(self, eef_name: str, subtask_index: int, env_cfg) -> str | None: + """ + (SkillGen) Return the expected attached object for the given EEF/subtask. + + Assumes 'stack' subtasks place the object grasped in the preceding 'grasp' subtask. + Returns None for 'grasp' (or others) at subtask start. + """ + if eef_name not in env_cfg.subtask_configs: + return None + + subtask_configs = env_cfg.subtask_configs[eef_name] + if not (0 <= subtask_index < len(subtask_configs)): + return None + + current_cfg = subtask_configs[subtask_index] + # If stacking, expect we are holding the object grasped in the prior subtask + if "stack" in str(current_cfg.subtask_term_signal).lower(): + if subtask_index > 0: + prev_cfg = subtask_configs[subtask_index - 1] + if "grasp" in str(prev_cfg.subtask_term_signal).lower(): + return prev_cfg.object_ref + return None diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_vm_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_vm_env_cfg.py new file mode 100644 index 000000000000..2aa9b7d15590 --- /dev/null +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_vm_env_cfg.py @@ -0,0 +1,162 @@ +# Copyright (c) 2024-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: Apache-2.0 + +from isaaclab.envs.mimic_env_cfg import MimicEnvCfg, SubTaskConfig +from isaaclab.utils import configclass + +from isaaclab_tasks.manager_based.manipulation.cube_lift.config.franka.dev_ik_rel_env_place_vismot import FrankaDevEnvVMCfg + + +@configclass +class CubeMimicVMEnvCfg(FrankaDevEnvVMCfg, MimicEnvCfg): + """ + Isaac Lab Mimic environment config class for Franka Cube Stack IK Rel env. + """ + + def __post_init__(self): + # post init of parents + super().__post_init__() + # # TODO: Figure out how we can move this to the MimicEnvCfg class + # # The __post_init__() above only calls the init for FrankaCubeStackEnvCfg and not MimicEnvCfg + # # https://stackoverflow.com/questions/59986413/achieving-multiple-inheritance-using-python-dataclasses + + # Override the existing values + self.datagen_config.name = "beaker_rand_scale_D0" + self.datagen_config.generation_guarantee = True + self.datagen_config.generation_keep_failed = False + self.datagen_config.generation_num_trials = 10 + self.datagen_config.generation_select_src_per_subtask = True + self.datagen_config.generation_transform_first_robot_pose = False + self.datagen_config.generation_interpolate_from_last_target_pose = True + self.datagen_config.generation_relative = True + self.datagen_config.max_num_failures = 100 + self.datagen_config.seed = 101 + + # The following are the subtask configurations for the stack task. + # The following are the subtask configurations for the stack task. + subtask_configs = [] + # subtask_configs.append( + # SubTaskConfig( + # # Each subtask involves manipulation with respect to a single object frame. + # object_ref="object", + # # This key corresponds to the binary indicator in "datagen_info" that signals + # # when this subtask is finished (e.g., on a 0 to 1 edge). + # subtask_term_signal="appr", + # # Specifies time offsets for data generation when splitting a trajectory into + # # subtask segments. Random offsets are added to the termination boundary. + # subtask_term_offset_range=(0, 0), + # # Selection strategy for the source subtask segment during data generation + # selection_strategy="nearest_neighbor_object", + # # Optional parameters for the selection strategy function + # selection_strategy_kwargs={"nn_k": 3}, + # # Amount of action noise to apply during this subtask + # action_noise=0.03, + # # Number of interpolation steps to bridge to this subtask segment + # num_interpolation_steps=0, + # # Additional fixed steps for the robot to reach the necessary pose + # num_fixed_steps=0, + # # If True, apply action noise during the interpolation phase and execution + # apply_noise_during_interpolation=False, + # ) + # ) + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # Corresponding key for the binary indicator in "datagen_info" for completion + subtask_term_signal="grasp", + # Time offsets for data generation when splitting a trajectory + subtask_term_offset_range=(10, 20), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + description="Grasp Glassware", + next_subtask_description="Stack glassware on scale", + ) + ) + #lift ? + # subtask_configs.append( + # SubTaskConfig( + # # Each subtask involves manipulation with respect to a single object frame. + # object_ref="object", + # # End of final subtask does not need to be detected + # subtask_term_signal="lift", + # # No time offsets for the final subtask + # subtask_term_offset_range=(0, 0), + # # Selection strategy for source subtask segment + # selection_strategy="nearest_neighbor_robot_distance", + # # Optional parameters for the selection strategy function + # selection_strategy_kwargs={"nn_k": 3}, + # # Amount of action noise to apply during this subtask + # action_noise=0.03, + # # Number of interpolation steps to bridge to this subtask segment + # num_interpolation_steps=0, + # # Additional fixed steps for the robot to reach the necessary pose + # num_fixed_steps=0, + # # If True, apply action noise during the interpolation phase and execution + # apply_noise_during_interpolation=False, + # ) + # ) + # subtask_configs.append( + # SubTaskConfig( + # # Each subtask involves manipulation with respect to a single object frame. + # object_ref="object", + # # End of final subtask does not need to be detected + # # This is actually the lift + # subtask_term_signal="appr_goal", + # # No time offsets for the final subtask + # subtask_term_offset_range=(10, 20), + # # Selection strategy for source subtask segment + # selection_strategy="nearest_neighbor_robot_distance", + # # Optional parameters for the selection strategy function + # selection_strategy_kwargs={"nn_k": 3}, + # # Amount of action noise to apply during this subtask + # action_noise=0.03, + # # Number of interpolation steps to bridge to this subtask segment + # num_interpolation_steps=5, + # # Additional fixed steps for the robot to reach the necessary pose + # num_fixed_steps=0, + # # If True, apply action noise during the interpolation phase and execution + # apply_noise_during_interpolation=False, + # ) + # ) + + subtask_configs.append( + SubTaskConfig( + # Each subtask involves manipulation with respect to a single object frame. + object_ref="object", + # End of final subtask - when using --annotate_subtask_start_signals, + # the last subtask must also have a signal name + subtask_term_signal="stacked", + # No time offsets for the final subtask + subtask_term_offset_range=(0, 0), + # Selection strategy for source subtask segment + selection_strategy="nearest_neighbor_object_distance", + # Optional parameters for the selection strategy function + selection_strategy_kwargs={"nn_k": 3}, + # Amount of action noise to apply during this subtask + action_noise=0.03, + # Number of interpolation steps to bridge to this subtask segment + num_interpolation_steps=5, + # Additional fixed steps for the robot to reach the necessary pose + num_fixed_steps=0, + # If True, apply action noise during the interpolation phase and execution + apply_noise_during_interpolation=False, + description="Stack glassware on scale", + ) + ) + + + + self.subtask_configs["franka"] = subtask_configs \ No newline at end of file diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py index 0c07f8dcb01b..d02f0da3a92a 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py @@ -157,6 +157,53 @@ disable_env_checker=True, ) +gym.register( + id="Dev-IK-Rel-Place-vision-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.dev_ik_rel_env_place_vismot:FrankaDevEnvVMCfg", + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), + "robomimic_bc_trans_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_trans.json"), + "robomimic_hbc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/hbc.json"), + "robomimic_bcq_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bcq.json"), + "robomimic_diffusion_policy_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/diffusion_policy.json"), + + }, + disable_env_checker=True, +) + +gym.register( + id="Dev-IK-Rel-Insert-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.dev_ik_rel_vial_insert:FrankaDevEnvCfg", + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), + "robomimic_bc_trans_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_trans.json"), + "robomimic_hbc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/hbc.json"), + "robomimic_bcq_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bcq.json"), + "robomimic_diffusion_policy_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/diffusion_policy.json"), + + }, + disable_env_checker=True, +) + + +gym.register( + id="Dev-IK-Rel-Insert-vision-v0", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.dev_ik_rel_env_insert_vismot:FrankaDevEnvVMCfg", + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), + "robomimic_bc_trans_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_trans.json"), + "robomimic_hbc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/hbc.json"), + "robomimic_bcq_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bcq.json"), + "robomimic_diffusion_policy_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/diffusion_policy.json"), + + }, + disable_env_checker=True, +) + + gym.register( id="Dev-IK-Rel-Place-Skillgen-v0", entry_point="isaaclab.envs:ManagerBasedRLEnv", diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json index 8125682666ea..6bde3e9661aa 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json @@ -1,13 +1,13 @@ { "algo_name": "bc", "experiment": { - "name": "bc_med_data_", + "name": "vial_insert", "validate": true, "logging": { "terminal_output_to_txt": true, "log_tb": true, "log_wandb": true, - "wandb_proj_name": "pick_place_bc_rnn_0.5_mix" + "wandb_proj_name": "vial_insert" }, "save": { "enabled": true, @@ -20,7 +20,7 @@ }, "epoch_every_n_steps": 100, "validation_epoch_every_n_steps": 10, - "env": "Dev-IK-Rel-place-v0", + "env": "Dev-IK-Rel-Insert-v0", "additional_envs": null, "render": false, "render_video": true, @@ -55,7 +55,7 @@ "goal_mode": null, "cuda": true, "batch_size": 100, - "num_epochs": 1500, + "num_epochs": 1000, "seed": 101 }, "algo": { diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_insert_vismot.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_insert_vismot.py new file mode 100755 index 000000000000..3c9ab028214c --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_insert_vismot.py @@ -0,0 +1,218 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass +from isaaclab.assets import RigidObjectCfg, ArticulationCfg +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.assets import AssetBaseCfg +from isaaclab.sim.spawners.from_files import UsdFileCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, NVIDIA_NUCLEUS_DIR +from source.isaaclab_assets.isaaclab_assets.robots.universal_robots import UR10_CFG +from . import dev_env_cfg +import isaaclab.sim as sim_utils +from isaaclab.sensors import CameraCfg +import math +from isaaclab.managers import SceneEntityCfg +from isaaclab_assets.glassware.glassware import ChemistryGlassware +from isaaclab_tasks.manager_based.manipulation.cube_lift import mdp +from isaaclab_tasks.manager_based.manipulation.cube_lift.mdp import franka_stack_events + +from isaaclab.managers import TerminationTermCfg as DoneTerm +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip +from isaaclab_assets.robots.universal_robots import UR10e_ROBOTIQ_GRIPPER_CFG + +## add some cameras in + + + +@configclass +class FrankaDevEnvVMCfg(dev_env_cfg.FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # put the beaker on the stir plate + glassware = ChemistryGlassware() + self.scene.stirplate = glassware.stirplate(pos=[0.5, 0.0, 0.01]) + #self.scene.scale = glassware.scale(pos=[0.3, -0.3, 0.01]) + self.scene.vialrack = glassware.line_vial_rack(pos=[0.3, -0.3, 0.01]) + self.scene.object = glassware.capped_vial(pos=[0.3, -0.3, 0.01], scale =1.0) + self.observations.policy.target_object_position = ObsTerm(func=mdp.target_position, params={"object_cfg": SceneEntityCfg("vialrack")}) + + ### subtask + self.observations.subtask_terms.stacked = ObsTerm( + func=mdp.object_stacked, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "upper_object_cfg": SceneEntityCfg("object"), + "lower_object_cfg": SceneEntityCfg("vialrack"), + }, + ) + + + + #### Vision scene + self.scene.wrist_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_hand/wrist_cam", + update_period=0.0, + height=84, + width=84, + data_types=["rgb", "distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 2) + ), + offset=CameraCfg.OffsetCfg( + pos=(0.13, 0.0, -0.15), rot=(-0.70614, 0.03701, 0.03701, -0.70614), convention="ros" + ), + ) + self.rerender_on_reset = True + self.sim.render.antialiasing_mode = "OFF" # disable dlss + + # List of image observations in policy observations + self.image_obs_list = ["table_cam", "wrist_cam"] + + + # Set table view camera + self.scene.table_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/table_cam", + update_period=0.0, + height=84, + width=84, + data_types=["rgb", "distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 2) + ), + offset=CameraCfg.OffsetCfg( + pos=(1.0, 0.0, 0.4), rot=(0.35355, -0.61237, -0.61237, 0.35355), convention="ros" + ), + ) + + + self.events.reset_object_position = EventTerm( + func=mdp.reset_place_root_state_uniform, + mode="reset", + params={ + "pose_range": {"x": (0, 0.2), "y": (0, 0.25), "z": (0.02, 0.02)}, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("object"), + "asset2_cfg" : SceneEntityCfg("vialrack"), + "asset3_cfg" : SceneEntityCfg("stirplate") + }, + ) + + #### VISION BASED EVENTS + self.events.randomize_light = EventTerm( + func=franka_stack_events.randomize_scene_lighting_domelight, + mode="reset", + params={ + "intensity_range": (1500.0, 10000.0), + "color_variation": 0.4, + "textures": [ + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Indoor/hospital_room_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Indoor/surgery_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Studio/photo_studio_01_4k.hdr", + ], + "default_intensity": 3000.0, + "default_color": (0.75, 0.75, 0.75), + "default_texture": "", + }, + ) + + self.events.randomize_table_visual_material = EventTerm( + func=franka_stack_events.randomize_visual_texture_material, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("table"), + "textures": [ + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Steel_Stainless/Steel_Stainless_BaseColor.png", + ], + "default_texture": ( + f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/Materials/Textures/DemoTable_TableBase_BaseColor.png" + ), + }, + ) + + # self.events.randomize_robot_arm_visual_texture = EventTerm( + # func=franka_stack_events.randomize_visual_texture_material, + # mode="reset", + # params={ + # "asset_cfg": SceneEntityCfg("robot"), + # "textures": [ + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Aluminum_Cast/Aluminum_Cast_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Aluminum_Polished/Aluminum_Polished_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Brass/Brass_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Bronze/Bronze_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Brushed_Antique_Copper/Brushed_Antique_Copper_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Cast_Metal_Silver_Vein/Cast_Metal_Silver_Vein_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Copper/Copper_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Gold/Gold_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Iron/Iron_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/RustedMetal/RustedMetal_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Silver/Silver_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Steel_Carbon/Steel_Carbon_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Steel_Stainless/Steel_Stainless_BaseColor.png", + # ], + # }, + # ) + + #### VISION BASED OBS + self.observations.policy.table_cam = ObsTerm( + func=mdp.image, params={"sensor_cfg": SceneEntityCfg("table_cam"), "data_type": "rgb", "normalize": False} + ) + self.observations.policy.wrist_cam = ObsTerm( + func=mdp.image, params={"sensor_cfg": SceneEntityCfg("wrist_cam"), "data_type": "rgb", "normalize": False} + ) + + + + #self.scene.robot = UR10e_ROBOTIQ_GRIPPER_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace( + prim_path="{ENV_REGEX_NS}/Robot", + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + + "panda_joint1": 0.3281, + "panda_joint2": -0.3684, + "panda_joint3": -0.2787, + "panda_joint4": -2.6138, + "panda_joint5": -2.7527, + "panda_joint6": 2.4991, # +90° → keeps hand level + "panda_joint7": 0.3331, + "panda_finger_joint1": 0.04, # open gripper + "panda_finger_joint2": 0.04, + } + ), + ) + # replace with relative position controller + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + ) + #self.terminations.success=DoneTerm(func=mdp.object_stacked_upright, params={"lower_object_cfg": SceneEntityCfg("scale")}) + + #self.observations.subtask_terms.appr_goal=ObsTerm(func=mdp.is_object_lifted, params={"threshold":0.15} + #) + + + +@configclass +class FrankaCubeEnvCfg_PLAY(FrankaDevEnvVMCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_obs.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_obs.py index 9404c5d07aae..c3fea274b1a4 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_obs.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_obs.py @@ -8,9 +8,11 @@ from isaaclab.utils import configclass from isaaclab.assets import RigidObjectCfg, ArticulationCfg from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.managers import TerminationTermCfg as DoneTerm from . import dev_env_cfg import math from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import ObservationTermCfg as ObsTerm from isaaclab_assets.glassware.glassware import ChemistryGlassware from isaaclab_tasks.manager_based.manipulation.cube_lift import mdp ## @@ -64,7 +66,15 @@ def __post_init__(self): scale=0.5, body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), ) - + self.observations.subtask_terms.appr_goal = ObsTerm( + func=mdp.object_near_goal, + params={ + "threshold": 0.05, + "command_name": "object_pose", + }, + ) + #self.observations.policy.target_object_position = ObsTerm(func=mdp.generated_command_position, params={"command_name": "object_pose"}) + self.terminations.success = DoneTerm(func=mdp.object_near_goal, params={"threshold": 0.05 }) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py index 223211fd0669..2884f74bd029 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py @@ -76,11 +76,20 @@ def __post_init__(self): scale=0.5, body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), ) - #self.terminations.success=DoneTerm(func=mdp.object_stacked_upright, params={"lower_object_cfg": SceneEntityCfg("scale")}) - - #self.observations.subtask_terms.appr_goal=ObsTerm(func=mdp.is_object_lifted, params={"threshold":0.15} - #) - + self.terminations.success=DoneTerm(func=mdp.object_stacked_upright, params={"lower_object_cfg": SceneEntityCfg("scale")}) + self.observations.policy.abs_joint_pos = ObsTerm(func=mdp.get_joint_pos) + self.observations.policy.object_knocked = ObsTerm(func=mdp.object_knocked) + self.observations.subtask_terms.appr_goal=ObsTerm(func=mdp.is_object_lifted, params={"threshold":0.15}) + + self.observations.policy.target_object_position = ObsTerm(func=mdp.target_position, params={"object_cfg": SceneEntityCfg("scale")}) + self.observations.subtask_terms.stacked = ObsTerm( + func=mdp.object_stacked, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "upper_object_cfg": SceneEntityCfg("object"), + "lower_object_cfg": SceneEntityCfg("scale"), + }, + ) @configclass diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place_vismot.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place_vismot.py new file mode 100755 index 000000000000..fabf3a6cfc84 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place_vismot.py @@ -0,0 +1,206 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass +from isaaclab.assets import RigidObjectCfg, ArticulationCfg +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.assets import AssetBaseCfg +from isaaclab.sim.spawners.from_files import UsdFileCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, NVIDIA_NUCLEUS_DIR +from source.isaaclab_assets.isaaclab_assets.robots.universal_robots import UR10_CFG +from . import dev_env_cfg +import isaaclab.sim as sim_utils +from isaaclab.sensors import CameraCfg +import math +from isaaclab.managers import SceneEntityCfg +from isaaclab_assets.glassware.glassware import ChemistryGlassware +from isaaclab_tasks.manager_based.manipulation.cube_lift import mdp +from isaaclab_tasks.manager_based.manipulation.cube_lift.mdp import franka_stack_events + +from isaaclab.managers import TerminationTermCfg as DoneTerm +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip +from isaaclab_assets.robots.universal_robots import UR10e_ROBOTIQ_GRIPPER_CFG + +## add some cameras in + + + +@configclass +class FrankaDevEnvVMCfg(dev_env_cfg.FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + + + # put the beaker on the stir plate + glassware = ChemistryGlassware() + self.scene.stirplate = glassware.stirplate(pos=[0.5, 0.0, 0.01]) + self.scene.scale = glassware.scale(pos=[0.3, -0.3, 0.01]) + + #### Vision scene + self.scene.wrist_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/Robot/panda_hand/wrist_cam", + update_period=0.0, + height=84, + width=84, + data_types=["rgb", "distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 2) + ), + offset=CameraCfg.OffsetCfg( + pos=(0.13, 0.0, -0.15), rot=(-0.70614, 0.03701, 0.03701, -0.70614), convention="ros" + ), + ) + self.rerender_on_reset = True + self.sim.render.antialiasing_mode = "OFF" # disable dlss + + # List of image observations in policy observations + self.image_obs_list = ["table_cam", "wrist_cam"] + + + # Set table view camera + self.scene.table_cam = CameraCfg( + prim_path="{ENV_REGEX_NS}/table_cam", + update_period=0.0, + height=84, + width=84, + data_types=["rgb", "distance_to_image_plane"], + spawn=sim_utils.PinholeCameraCfg( + focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 2) + ), + offset=CameraCfg.OffsetCfg( + pos=(1.0, 0.0, 0.4), rot=(0.35355, -0.61237, -0.61237, 0.35355), convention="ros" + ), + ) + + + self.events.reset_object_position = EventTerm( + func=mdp.reset_place_root_state_uniform, + mode="reset", + params={ + "pose_range": {"x": (0, 0.2), "y": (0, 0.25), "z": (0.02, 0.02)}, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("object", body_names="Object"), + "asset2_cfg" : SceneEntityCfg("stirplate"), + "asset3_cfg" : SceneEntityCfg("scale") + + }, + ) + + #### VISION BASED EVENTS + self.events.randomize_light = EventTerm( + func=franka_stack_events.randomize_scene_lighting_domelight, + mode="reset", + params={ + "intensity_range": (1500.0, 10000.0), + "color_variation": 0.4, + "textures": [ + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Indoor/hospital_room_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Indoor/surgery_4k.hdr", + f"{NVIDIA_NUCLEUS_DIR}/Assets/Skies/Studio/photo_studio_01_4k.hdr", + ], + "default_intensity": 3000.0, + "default_color": (0.75, 0.75, 0.75), + "default_texture": "", + }, + ) + + self.events.randomize_table_visual_material = EventTerm( + func=franka_stack_events.randomize_visual_texture_material, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("table"), + "textures": [ + f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Steel_Stainless/Steel_Stainless_BaseColor.png", + ], + "default_texture": ( + f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/Materials/Textures/DemoTable_TableBase_BaseColor.png" + ), + }, + ) + + # self.events.randomize_robot_arm_visual_texture = EventTerm( + # func=franka_stack_events.randomize_visual_texture_material, + # mode="reset", + # params={ + # "asset_cfg": SceneEntityCfg("robot"), + # "textures": [ + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Aluminum_Cast/Aluminum_Cast_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Aluminum_Polished/Aluminum_Polished_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Brass/Brass_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Bronze/Bronze_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Brushed_Antique_Copper/Brushed_Antique_Copper_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Cast_Metal_Silver_Vein/Cast_Metal_Silver_Vein_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Copper/Copper_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Gold/Gold_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Iron/Iron_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/RustedMetal/RustedMetal_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Silver/Silver_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Steel_Carbon/Steel_Carbon_BaseColor.png", + # f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Steel_Stainless/Steel_Stainless_BaseColor.png", + # ], + # }, + # ) + + #### VISION BASED OBS + self.observations.policy.table_cam = ObsTerm( + func=mdp.image, params={"sensor_cfg": SceneEntityCfg("table_cam"), "data_type": "rgb", "normalize": False} + ) + self.observations.policy.wrist_cam = ObsTerm( + func=mdp.image, params={"sensor_cfg": SceneEntityCfg("wrist_cam"), "data_type": "rgb", "normalize": False} + ) + + + + #self.scene.robot = UR10e_ROBOTIQ_GRIPPER_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace( + prim_path="{ENV_REGEX_NS}/Robot", + init_state=ArticulationCfg.InitialStateCfg( + joint_pos={ + + "panda_joint1": 0.3281, + "panda_joint2": -0.3684, + "panda_joint3": -0.2787, + "panda_joint4": -2.6138, + "panda_joint5": -2.7527, + "panda_joint6": 2.4991, # +90° → keeps hand level + "panda_joint7": 0.3331, + "panda_finger_joint1": 0.04, # open gripper + "panda_finger_joint2": 0.04, + } + ), + ) + # replace with relative position controller + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + ) + #self.terminations.success=DoneTerm(func=mdp.object_stacked_upright, params={"lower_object_cfg": SceneEntityCfg("scale")}) + + #self.observations.subtask_terms.appr_goal=ObsTerm(func=mdp.is_object_lifted, params={"threshold":0.15} + #) + + + +@configclass +class FrankaCubeEnvCfg_PLAY(FrankaDevEnvVMCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_vial_insert.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_vial_insert.py index 40a0566b68d5..35707cb590d8 100644 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_vial_insert.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_vial_insert.py @@ -11,13 +11,18 @@ from isaaclab.managers import EventTermCfg as EventTerm from isaaclab.assets import AssetBaseCfg from isaaclab.sim.spawners.from_files import UsdFileCfg -from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR -from source.isaaclab_assets.isaaclab_assets.robots.universal_robots import UR10_CFG +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, NVIDIA_NUCLEUS_DIR +#from source.isaaclab_assets.isaaclab_assets.robots.universal_robots import UR10_CFG +from isaaclab_assets.robots.universal_robots import UR10_CFG from . import dev_env_cfg +import isaaclab.sim as sim_utils +from isaaclab.sensors import CameraCfg import math from isaaclab.managers import SceneEntityCfg from isaaclab_assets.glassware.glassware import ChemistryGlassware from isaaclab_tasks.manager_based.manipulation.cube_lift import mdp +from isaaclab_tasks.manager_based.manipulation.cube_lift.mdp import franka_stack_events + from isaaclab.managers import TerminationTermCfg as DoneTerm ## # Pre-defined configs @@ -25,6 +30,8 @@ from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip from isaaclab_assets.robots.universal_robots import UR10e_ROBOTIQ_GRIPPER_CFG +## add some cameras in + @configclass @@ -32,25 +39,44 @@ class FrankaDevEnvCfg(dev_env_cfg.FrankaDevEnvCfg): def __post_init__(self): # post init of parent super().__post_init__() - # get the vial from the stirplate and place in vial rack + # put the beaker on the stir plate glassware = ChemistryGlassware() - self.scene.object=glassware.vial(pos=[0.5, 0.0, 0.02],name="Object") self.scene.stirplate = glassware.stirplate(pos=[0.5, 0.0, 0.01]) + #self.scene.scale = glassware.scale(pos=[0.3, -0.3, 0.01]) + self.scene.vialrack = glassware.line_vial_rack(pos=[0.3, -0.3, 0.0], scale=1.5) + self.scene.object = glassware.capped_vial(pos=[0.5, 0.0, 0.01], scale =1.0, name="object") + self.observations.policy.target_object_position = ObsTerm(func=mdp.target_position, params={"object_cfg": SceneEntityCfg("vialrack")}) + + ### subtask + self.observations.subtask_terms.stacked = ObsTerm( + func=mdp.object_stacked, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "upper_object_cfg": SceneEntityCfg("object"), + "lower_object_cfg": SceneEntityCfg("vialrack"), + }, + ) - self.scene.vialrack = glassware.vialrack(pos=[0.3, -0.3, 0.01]) - + self.events.randomise_object_scale= EventTerm( + func=mdp.randomize_rigid_body_scale, + mode="prestartup", + params={ + "scale_range": {"x": (1.0, 1.0), "y": (1.0, 1.0), "z": (1.0, 1.0)}, + "asset_cfg": SceneEntityCfg("object"), + }, + ) self.events.reset_object_position = EventTerm( func=mdp.reset_place_root_state_uniform, mode="reset", params={ "pose_range": {"x": (0, 0.2), "y": (0, 0.25), "z": (0.02, 0.02)}, "velocity_range": {}, - "asset_cfg": SceneEntityCfg("object", body_names="Object"), + "asset_cfg": SceneEntityCfg("object"), "asset2_cfg" : SceneEntityCfg("stirplate"), - "asset3_cfg" : SceneEntityCfg("scale") - + "asset3_cfg" : SceneEntityCfg("vialrack") }, ) + #self.scene.robot = UR10e_ROBOTIQ_GRIPPER_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace( prim_path="{ENV_REGEX_NS}/Robot", @@ -78,10 +104,14 @@ def __post_init__(self): scale=0.5, body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), ) - self.terminations.success=DoneTerm(func=mdp.object_inserted_upright, params={"lower_object_cfg": SceneEntityCfg("vialrack")}) - #self.observations.subtask_terms.appr_goal=ObsTerm(func=mdp.is_object_lifted, params={"threshold":0.15} - #) + self.terminations.success= DoneTerm(func=mdp.object_inserted_upright, params={"lower_object_cfg": SceneEntityCfg("vialrack")}) + + #self.terminations.success=DoneTerm(func=mdp.object_stacked_upright, params={"lower_object_cfg": SceneEntityCfg("scale")}) + + self.observations.subtask_terms.appr_goal=ObsTerm(func=mdp.is_object_lifted, params={"threshold":0.15} + + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py index 9b74ea1e1364..07ff54a32231 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py @@ -136,8 +136,8 @@ class PolicyCfg(ObsGroup): joint_pos = ObsTerm(func=mdp.joint_pos_rel) joint_vel = ObsTerm(func=mdp.joint_vel_rel) object_position = ObsTerm(func=mdp.object_position_in_robot_root_frame) - #target_object_position = ObsTerm(func=mdp.generated_commands, params={"command_name": "object_pose"}) - target_object_position = ObsTerm(func=mdp.target_position, params={"object_cfg": SceneEntityCfg("scale")}) + target_object_position = ObsTerm(func=mdp.generated_commands, params={"command_name": "object_pose"}) + #target_object_position = ObsTerm(func=mdp.target_position, params={"object_cfg": SceneEntityCfg("scale")}) actions = ObsTerm(func=mdp.last_action) # object_to_target = ObsTerm(func=mdp.object_near_goal) eef_pos = ObsTerm(func=mdp.ee_frame_pos) @@ -182,14 +182,7 @@ class SubtaskCfg(ObsGroup): "object_cfg": SceneEntityCfg("object"), }, ) - stacked = ObsTerm( - func=mdp.object_stacked, - params={ - "robot_cfg": SceneEntityCfg("robot"), - "upper_object_cfg": SceneEntityCfg("object"), - "lower_object_cfg": SceneEntityCfg("scale"), - }, - ) + # lift = ObsTerm( # func=mdp.is_object_lifted, # params={ @@ -197,13 +190,7 @@ class SubtaskCfg(ObsGroup): # "threshold" : 0.1 # } # ) - # appr_goal = ObsTerm( - # func=mdp.object_near_goal, - # params={ - # "threshold": 0.05, - # "command_name": "object_pose", - # }, - # ) + # release_object = ObsTerm( # func=mdp.object_released, # params={ @@ -233,16 +220,18 @@ class EventCfg: # self.reset_all.params["loghelper"] = loghelper reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset") - reset_object_position = EventTerm( - func=mdp.reset_root_state_uniform, - mode="reset", - params={ - "pose_range": {"x": (0, 0.2), "y": (0, 0.25), "z": (0.02, 0.05)}, - "velocity_range": {}, - "asset_cfg": SceneEntityCfg("object", body_names="Object"), - }, - ) + # reset_object_position = EventTerm( + # func=mdp.reset_root_state_uniform, + # mode="reset", + # params={ + # "pose_range": {"x": (0, 0.2), "y": (0, 0.25), "z": (0.02, 0.05)}, + # "velocity_range": {}, + # "asset_cfg": SceneEntityCfg("object", body_names="Object"), + # }, + # ) #randomises the scale of the object + + #set this to 1.0 for the vial! randomise_object_scale = EventTerm( func=mdp.randomize_rigid_body_scale, mode="prestartup", @@ -363,7 +352,7 @@ def __post_init__(self): # general settings self.decimation = 2 - self.episode_length_s = 500 + self.episode_length_s = 5000 # simulation settings self.sim.dt = 0.01 # 100Hz self.sim.render_interval = self.decimation diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/franka_stack_events.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/franka_stack_events.py new file mode 100644 index 000000000000..009a44b1b372 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/franka_stack_events.py @@ -0,0 +1,314 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + + +from __future__ import annotations + +import math +import random +import torch +from typing import TYPE_CHECKING + +from isaacsim.core.utils.extensions import enable_extension + +import isaaclab.utils.math as math_utils +from isaaclab.assets import Articulation, AssetBase +from isaaclab.managers import SceneEntityCfg + +if TYPE_CHECKING: + from isaaclab.envs import ManagerBasedEnv + + +def set_default_joint_pose( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + default_pose: torch.Tensor, + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +): + # Set the default pose for robots in all envs + asset = env.scene[asset_cfg.name] + asset.data.default_joint_pos = torch.tensor(default_pose, device=env.device).repeat(env.num_envs, 1) + + +def randomize_joint_by_gaussian_offset( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + mean: float, + std: float, + asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), +): + asset: Articulation = env.scene[asset_cfg.name] + + # Add gaussian noise to joint states + joint_pos = asset.data.default_joint_pos[env_ids].clone() + joint_vel = asset.data.default_joint_vel[env_ids].clone() + joint_pos += math_utils.sample_gaussian(mean, std, joint_pos.shape, joint_pos.device) + + # Clamp joint pos to limits + joint_pos_limits = asset.data.soft_joint_pos_limits[env_ids] + joint_pos = joint_pos.clamp_(joint_pos_limits[..., 0], joint_pos_limits[..., 1]) + + # Don't noise the gripper poses + joint_pos[:, -2:] = asset.data.default_joint_pos[env_ids, -2:] + + # Set into the physics simulation + asset.set_joint_position_target(joint_pos, env_ids=env_ids) + asset.set_joint_velocity_target(joint_vel, env_ids=env_ids) + asset.write_joint_state_to_sim(joint_pos, joint_vel, env_ids=env_ids) + + +def sample_random_color(base=(0.75, 0.75, 0.75), variation=0.1): + """ + Generates a randomized color that stays close to the base color while preserving overall brightness. + The relative balance between the R, G, and B components is maintained by ensuring that + the sum of random offsets is zero. + + Parameters: + base (tuple): The base RGB color with each component between 0 and 1. + variation (float): Maximum deviation to sample for each channel before balancing. + + Returns: + tuple: A new RGB color with balanced random variation. + """ + # Generate random offsets for each channel in the range [-variation, variation] + offsets = [random.uniform(-variation, variation) for _ in range(3)] + # Compute the average offset + avg_offset = sum(offsets) / 3 + # Adjust offsets so their sum is zero (maintaining brightness) + balanced_offsets = [offset - avg_offset for offset in offsets] + + # Apply the balanced offsets to the base color and clamp each channel between 0 and 1 + new_color = tuple(max(0, min(1, base_component + offset)) for base_component, offset in zip(base, balanced_offsets)) + + return new_color + + +def randomize_scene_lighting_domelight( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + intensity_range: tuple[float, float], + color_variation: float, + textures: list[str], + default_intensity: float = 3000.0, + default_color: tuple[float, float, float] = (0.75, 0.75, 0.75), + default_texture: str = "", + asset_cfg: SceneEntityCfg = SceneEntityCfg("light"), +): + asset: AssetBase = env.scene[asset_cfg.name] + light_prim = asset.prims[0] + + intensity_attr = light_prim.GetAttribute("inputs:intensity") + intensity_attr.Set(default_intensity) + + color_attr = light_prim.GetAttribute("inputs:color") + color_attr.Set(default_color) + + texture_file_attr = light_prim.GetAttribute("inputs:texture:file") + texture_file_attr.Set(default_texture) + + if not hasattr(env.cfg, "eval_mode") or not env.cfg.eval_mode: + return + + if env.cfg.eval_type in ["light_intensity", "all"]: + # Sample new light intensity + new_intensity = random.uniform(intensity_range[0], intensity_range[1]) + # Set light intensity to light prim + intensity_attr.Set(new_intensity) + + if env.cfg.eval_type in ["light_color", "all"]: + # Sample new light color + new_color = sample_random_color(base=default_color, variation=color_variation) + # Set light color to light prim + color_attr.Set(new_color) + + if env.cfg.eval_type in ["light_texture", "all"]: + # Sample new light texture (background) + new_texture = random.sample(textures, 1)[0] + # Set light texture to light prim + texture_file_attr.Set(new_texture) + + +def sample_object_poses( + num_objects: int, + min_separation: float = 0.0, + pose_range: dict[str, tuple[float, float]] = {}, + max_sample_tries: int = 5000, +): + range_list = [pose_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z", "roll", "pitch", "yaw"]] + pose_list = [] + + for i in range(num_objects): + for j in range(max_sample_tries): + sample = [random.uniform(range[0], range[1]) for range in range_list] + + # Accept pose if it is the first one, or if reached max num tries + if len(pose_list) == 0 or j == max_sample_tries - 1: + pose_list.append(sample) + break + + # Check if pose of object is sufficiently far away from all other objects + separation_check = [math.dist(sample[:3], pose[:3]) > min_separation for pose in pose_list] + if False not in separation_check: + pose_list.append(sample) + break + + return pose_list + + +def randomize_object_pose( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + asset_cfgs: list[SceneEntityCfg], + min_separation: float = 0.0, + pose_range: dict[str, tuple[float, float]] = {}, + max_sample_tries: int = 5000, +): + if env_ids is None: + return + + # Randomize poses in each environment independently + for cur_env in env_ids.tolist(): + pose_list = sample_object_poses( + num_objects=len(asset_cfgs), + min_separation=min_separation, + pose_range=pose_range, + max_sample_tries=max_sample_tries, + ) + + # Randomize pose for each object + for i in range(len(asset_cfgs)): + asset_cfg = asset_cfgs[i] + asset = env.scene[asset_cfg.name] + + # Write pose to simulation + pose_tensor = torch.tensor([pose_list[i]], device=env.device) + positions = pose_tensor[:, 0:3] + env.scene.env_origins[cur_env, 0:3] + orientations = math_utils.quat_from_euler_xyz(pose_tensor[:, 3], pose_tensor[:, 4], pose_tensor[:, 5]) + asset.write_root_pose_to_sim( + torch.cat([positions, orientations], dim=-1), env_ids=torch.tensor([cur_env], device=env.device) + ) + asset.write_root_velocity_to_sim( + torch.zeros(1, 6, device=env.device), env_ids=torch.tensor([cur_env], device=env.device) + ) + + +def randomize_rigid_objects_in_focus( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + asset_cfgs: list[SceneEntityCfg], + out_focus_state: torch.Tensor, + min_separation: float = 0.0, + pose_range: dict[str, tuple[float, float]] = {}, + max_sample_tries: int = 5000, +): + if env_ids is None: + return + + # List of rigid objects in focus for each env (dim = [num_envs, num_rigid_objects]) + env.rigid_objects_in_focus = [] + + for cur_env in env_ids.tolist(): + # Sample in focus object poses + pose_list = sample_object_poses( + num_objects=len(asset_cfgs), + min_separation=min_separation, + pose_range=pose_range, + max_sample_tries=max_sample_tries, + ) + + selected_ids = [] + for asset_idx in range(len(asset_cfgs)): + asset_cfg = asset_cfgs[asset_idx] + asset = env.scene[asset_cfg.name] + + # Randomly select an object to bring into focus + object_id = random.randint(0, asset.num_objects - 1) + selected_ids.append(object_id) + + # Create object state tensor + object_states = torch.stack([out_focus_state] * asset.num_objects).to(device=env.device) + pose_tensor = torch.tensor([pose_list[asset_idx]], device=env.device) + positions = pose_tensor[:, 0:3] + env.scene.env_origins[cur_env, 0:3] + orientations = math_utils.quat_from_euler_xyz(pose_tensor[:, 3], pose_tensor[:, 4], pose_tensor[:, 5]) + object_states[object_id, 0:3] = positions + object_states[object_id, 3:7] = orientations + + asset.write_object_state_to_sim( + object_state=object_states, env_ids=torch.tensor([cur_env], device=env.device) + ) + + env.rigid_objects_in_focus.append(selected_ids) + + +def randomize_visual_texture_material( + env: ManagerBasedEnv, + env_ids: torch.Tensor, + asset_cfg: SceneEntityCfg, + textures: list[str], + default_texture: str = "", + texture_rotation: tuple[float, float] = (0.0, 0.0), +): + """Randomize the visual texture of bodies on an asset using Replicator API. + + This function randomizes the visual texture of the bodies of the asset using the Replicator API. + The function samples random textures from the given texture paths and applies them to the bodies + of the asset. The textures are projected onto the bodies and rotated by the given angles. + + .. note:: + The function assumes that the asset follows the prim naming convention as: + "{asset_prim_path}/{body_name}/visuals" where the body name is the name of the body to + which the texture is applied. This is the default prim ordering when importing assets + from the asset converters in Isaac Lab. + + .. note:: + When randomizing the texture of individual assets, please make sure to set + :attr:`isaaclab.scene.InteractiveSceneCfg.replicate_physics` to False. This ensures that physics + parser will parse the individual asset properties separately. + """ + if hasattr(env.cfg, "eval_mode") and ( + not env.cfg.eval_mode or env.cfg.eval_type not in [f"{asset_cfg.name}_texture", "all"] + ): + return + # textures = [default_texture] + + # enable replicator extension if not already enabled + enable_extension("omni.replicator.core") + # we import the module here since we may not always need the replicator + import omni.replicator.core as rep + + # check to make sure replicate_physics is set to False, else raise error + # note: We add an explicit check here since texture randomization can happen outside of 'prestartup' mode + # and the event manager doesn't check in that case. + if env.cfg.scene.replicate_physics: + raise RuntimeError( + "Unable to randomize visual texture material with scene replication enabled." + " For stable USD-level randomization, please disable scene replication" + " by setting 'replicate_physics' to False in 'InteractiveSceneCfg'." + ) + + # convert from radians to degrees + texture_rotation = tuple(math.degrees(angle) for angle in texture_rotation) + + # obtain the asset entity + asset = env.scene[asset_cfg.name] + + # join all bodies in the asset + body_names = asset_cfg.body_names + if isinstance(body_names, str): + body_names_regex = body_names + elif isinstance(body_names, list): + body_names_regex = "|".join(body_names) + else: + body_names_regex = ".*" + + if not hasattr(asset, "cfg"): + prims_group = rep.get.prims(path_pattern=f"{asset.prim_paths[0]}/visuals") + else: + prims_group = rep.get.prims(path_pattern=f"{asset.cfg.prim_path}/{body_names_regex}/visuals") + + with prims_group: + rep.randomizer.texture( + textures=textures, project_uvw=True, texture_rotate=rep.distribution.uniform(*texture_rotation) + ) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py index 119f5ef00407..a74a3b97cd11 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py @@ -183,7 +183,7 @@ def gripper_pos(env: ManagerBasedRLEnv, robot_cfg: SceneEntityCfg = SceneEntityC def object_near_goal( env: ManagerBasedRLEnv, command_name: str = "object_pose", - threshold: float = 0.05, + threshold: float = 0.1, robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), object_cfg: SceneEntityCfg = SceneEntityCfg("object"), # loghelper : LoggingHelper = LoggingHelper() @@ -333,6 +333,14 @@ def target_pose( object_quat_w = object.data.root_quat_w # (num_envs, 4) - already wxyz format return torch.cat([object_pos_w, object_quat_w], dim=-1) # (num_envs, 7) +def generated_command_position( + env: ManagerBasedRLEnv, + command_name: str +) -> torch.Tensor: + """The generated command position (first 3 elements).""" + command = env.command_manager.get_command(command_name) + return command[:, :3] + # def object_distance_to_goal( # env: ManagerBasedRLEnv, # object_cfg: SceneEntityCfg = SceneEntityCfg("object"), diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/terminations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/terminations.py index fa2ab92788f9..399ab972ffeb 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/terminations.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/terminations.py @@ -52,7 +52,7 @@ def object_reached_goal( des_pos_w, _ = combine_frame_transforms(robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], des_pos_b) # distance of the end-effector to the object: (num_envs,) distance = torch.norm(des_pos_w - object.data.root_pos_w[:, :3], dim=1) - # print(f"For DEBUG : DISTANCE TO GOAL : {distance}") + print(f"For DEBUG : DISTANCE TO GOAL : {distance}") # if(distance.item() < threshold): # loghelper.logsubtask(LogType.FINISH) test = distance < threshold @@ -160,6 +160,7 @@ def object_stacked(env: ManagerBasedRLEnv, robot_cfg: SceneEntityCfg = SceneEnti pos_diff = upper_object.data.root_pos_w - lower_object.data.root_pos_w height_dist = torch.linalg.vector_norm(pos_diff[:, 2:], dim=1) xy_dist = torch.linalg.vector_norm(pos_diff[:, :2], dim=1) + #print(f"For DEBUG : xy_dist : {xy_dist}, height_dist : {height_dist}") stacked = torch.logical_and(xy_dist < xy_threshold, (height_dist - height_diff) < height_threshold) # stacked = torch.logical_and(torch.isclose(robot.data.joint_pos[:, -1], # gripper_open_val.to(env.device), atol=1e-4, rtol=1e-4), stacked) @@ -170,7 +171,7 @@ def object_stacked(env: ManagerBasedRLEnv, robot_cfg: SceneEntityCfg = SceneEnti def object_inserted_upright(env: ManagerBasedRLEnv, robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), upper_object_cfg: SceneEntityCfg = SceneEntityCfg("object"),lower_object_cfg: SceneEntityCfg = SceneEntityCfg("vialrack"), - xy_threshold: float = 0.01, height_threshold: float = 0.01, height_diff: float = 0.05, + xy_threshold: float = 0.01, height_threshold: float = 0.03, height_diff: float = 0.0, atol=0.0001, rtol=0.0001, upright_good_deg: float = 30.0, gripper_open_val: torch.Tensor = torch.tensor([0.04]), logging=False) -> torch.Tensor: @@ -183,6 +184,7 @@ def object_inserted_upright(env: ManagerBasedRLEnv, robot_cfg: SceneEntityCfg = upright_good = tilt_deg <= upright_good_deg stacked = object_stacked(env, robot_cfg, upper_object_cfg, lower_object_cfg, xy_threshold, height_threshold, height_diff, gripper_open_val) + #print(f"For DEBUG : STACKED STATUS : {stacked}") stacked_upright = stacked & upright_good gripper_joint_ids, _ = robot.find_joints(env.cfg.gripper_joint_names) stacked = torch.logical_and( @@ -201,7 +203,7 @@ def object_inserted_upright(env: ManagerBasedRLEnv, robot_cfg: SceneEntityCfg = atol=atol, rtol=rtol, ), - stacked, + stacked_upright, ) #if logging: @@ -210,4 +212,4 @@ def object_inserted_upright(env: ManagerBasedRLEnv, robot_cfg: SceneEntityCfg = #if idx.numel() > 0: # print(f"[stacked-upright tilt] n={int(idx.numel())} sample={tilt_deg[idx][:5].tolist()}") - return inserted \ No newline at end of file + return stacked \ No newline at end of file From a8a7c5dbde098fc0af78971fdd9265c3eaafd6c1 Mon Sep 17 00:00:00 2001 From: LJones-alt Date: Fri, 6 Mar 2026 14:25:45 +0000 Subject: [PATCH 13/15] fixed comms --- .vscode/browse.vc.db | Bin 688128 -> 0 bytes docs/find_best_checkpoints.py | 2 +- .../vial_insert/20260216134807/config.json | 236 +++++++++ docs/run_experiments.sh | 30 +- .../teleoperation/teleop_se3_agent.py | 12 + .../robomimic/backup_controller_handler.py | 33 +- .../robomimic/backup_controller_insert.py | 163 ++++-- .../robomimic/backup_controller_insert_top.py | 478 ++++++++++++++++++ .../robomimic/play_controller.py | 2 +- .../robomimic/play_ensemble_v05.py | 62 ++- .../robomimic/play_with_backup.py | 2 +- .../robomimic/switching_logic_algorithm.tex | 62 +++ .../robomimic/train_ensemble.py | 4 +- scripts/tools/record_SM_demos.py | 364 +++++++++++++ scripts/tools/start_discovery_server.sh | 32 ++ .../isaaclab/envs/mdp/observations.py | 1 + .../isaaclab_mimic/datagen/generation.py | 6 +- .../isaaclab_mimic/envs/cube_mimic_env_cfg.py | 2 +- .../cube_lift/config/franka/__init__.py | 14 + .../agents/robomimic/bc_rnn_low_dim.json | 4 +- .../config/franka/dev_ik_rel_env_place.py | 2 +- .../config/franka/dev_ik_rel_vial_insert.py | 4 +- .../franka/dev_ik_rel_vial_insert_top_down.py | 127 +++++ .../manipulation/cube_lift/lift_env_cfg.py | 2 +- .../cube_lift/mdp/observations.py | 12 +- .../cube_lift/mdp/terminations.py | 9 +- 26 files changed, 1558 insertions(+), 107 deletions(-) delete mode 100644 .vscode/browse.vc.db create mode 100644 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zA=mYU0Ku{JDw6h!q?}%vB{YeGAeAnuilp4}k<-hpgtbdPZ_|X4(~G8rHMwk>sCPXH zjfRGTuAXe7TsjZ!A9;b3uv%0Jl72A_z22LV!8gkG$|Ygdg3~cwWY^o0)Z3!dpVcC) z6xD)+?RXQLzZ=sK>|x5niXK6gAamZJLbkG$_ks5h15pI*0^HrxnlQIBI4>{EgmSxoD4lMwlRb)r^~ zvTEavQl6xTTP74{`j}8VE{Oe0BY^Dhs}Zcb1u>ZIn--pj)JtUqORpdC zKd3Tg>Bel97nuk;2v7emYpa${z0<=g*UP%t zrgoq0+qP}ib5={uwB-vHzxmB(m1(N_2i1P#ORIiXwW0D&hTj@?=|8Qnhib}?A^}AL ziUbr1C=yU4ph!TGfFc1!0*V9_2`Ca!B%nwjM+rQaadG89q5078WF?(<}$7zx*$JEo9tbzKH z|3!iN|21bHIzf7V9VmzSZj4L65pL;DO;Qujl5&Sq z-GlxGfC!8>r(YDdHo None: if teleoperation_active: # process actions actions = action.repeat(env.num_envs, 1) + # process_action(actions) # apply actions env.step(actions) else: @@ -255,6 +262,11 @@ def stop_teleoperation() -> None: env.close() print("Environment closed") +def process_action(action): + ## handler that will spit out ROS2 commands + + print(f"Teleop Actions : {action}") + if __name__ == "__main__": # run the main function diff --git a/scripts/imitation_learning/robomimic/backup_controller_handler.py b/scripts/imitation_learning/robomimic/backup_controller_handler.py index f6e63d51df63..7edbdf5847f6 100644 --- a/scripts/imitation_learning/robomimic/backup_controller_handler.py +++ b/scripts/imitation_learning/robomimic/backup_controller_handler.py @@ -6,6 +6,7 @@ from backup_controller import BackupControllerSM from backup_controller_place import BackupControllerPlaceSM from backup_controller_insert import BackupControllerInsertSM +from backup_controller_insert_top import BackupControllerInsertTopSM import torch @@ -41,7 +42,10 @@ def _get_controller(self): print(f"[DEBUG] Using Place Backup Controller") return BackupControllerPlaceSM(dt=0.01*2, num_envs=self.num_envs, position_threshold=0.02, device='cuda', offset = torch.tensor([[-0.1, 0, 0.1]], device='cuda:0'), goal_quat = self.object_goal_rot, goal_pos= self.object_goal_pos) case "insert": - return BackupControllerInsertSM(0.01*2, self.num_envs,'cuda' , 0.02) + return BackupControllerInsertSM(dt=0.01*2, num_envs=self.num_envs, position_threshold=0.02, device='cuda', offset = torch.tensor([[-0.1, 0, 0.1]], device='cuda:0'), goal_quat = self.object_goal_rot, goal_pos= self.object_goal_pos) + case "insert_top": + ### check this quat + return BackupControllerInsertTopSM(dt=0.01*2, num_envs=self.num_envs, position_threshold=0.02, device='cuda', offset = torch.tensor([[-0.1, 0, 0.1]], device='cuda:0'), goal_quat = self.object_goal_rot, goal_pos= self.object_goal_pos) case _: raise ValueError(f"Unknown task type {self.tasktype} for backup controller") @@ -55,7 +59,9 @@ def _get_goal_pos(self): goal_pos = self.env.unwrapped.scene["scale"].data.root_pose_w # print(f"[DEBUG] Getting goal pos for place task {goal_pos}") return self.env.unwrapped.scene["scale"].data.root_pose_w - + case "insert_top": + # still vial rack + return self.env.unwrapped.scene["vialrack"].data.root_pose_w case "insert": return self.env.unwrapped.scene["vialrack"].data.root_pose_w @@ -69,9 +75,20 @@ def _get_goal_rot(self): goal_rot = self.env.unwrapped.command_manager.get_command("object_pose") print(f"[DEBUG] Getting goal quat for place task {goal_rot}") return self.env.unwrapped.command_manager.get_command("object_pose") - + case "insert_top": + object_pos = self.env.unwrapped.scene["object"].data.root_pose_w + rest_rot = torch.tensor([[ 0, 1, 0, 0]], device=self.device) + + goal_rot = torch.cat([object_pos[:, :3],rest_rot], dim =-1) + example = self.env.unwrapped.command_manager.get_command("object_pose") + print(f"example {example}") + #goal_rot= self.env.unwrapped.scene["object"].data.root_com_pose_w + print(f"Goal rot : {goal_rot}") + #goal_rot = torch.tensor([[0,1,0,0]], device=self.device)#self.env.unwrapped.command_manager.get_command("object_pose") + return goal_rot #self.env.unwrapped.command_manager.get_command("object_pose") case "insert": - return self.env.unwrapped.scene["vialrack"].data.root_pose_w + goal_rot = self.env.unwrapped.command_manager.get_command("object_pose") + return self.env.unwrapped.command_manager.get_command("object_pose") def reset(self): self.backup_controller.reset_idx() @@ -89,9 +106,15 @@ def _get_rest_pos(self): # lets change this into the 6 element tensor they are expecting roll,pitch,yaw = euler_xyz_from_quat(ee_recovery_rot) rest_pos = torch.cat([rest_pos, ee_recovery_rot], dim =-1) + + #print(f"rpy version :{torch.cat([rest_pos,roll.unsqueeze(0), pitch.unsqueeze(0), yaw.unsqueeze(0)], dim =-1)}") + if self.tasktype == "insert_top": + rest_pos = torch.tensor([[ 0.6226, -0.0621, 0.3555]], device=self.device) + rest_rot = torch.tensor([[ 0, 1, 0, 0]], device=self.device) + roll,pitch,yaw = euler_xyz_from_quat(rest_rot) + rest_pos = torch.cat([rest_pos,rest_rot], dim =-1) print(f"Rest pos {rest_pos}") print(f"rpy : {roll}, {pitch}, {yaw}") - #print(f"rpy version :{torch.cat([rest_pos,roll.unsqueeze(0), pitch.unsqueeze(0), yaw.unsqueeze(0)], dim =-1)}") return rest_pos def _setup_robot(self): diff --git a/scripts/imitation_learning/robomimic/backup_controller_insert.py b/scripts/imitation_learning/robomimic/backup_controller_insert.py index 5c12bdd60aea..51dfa4f2ca4c 100644 --- a/scripts/imitation_learning/robomimic/backup_controller_insert.py +++ b/scripts/imitation_learning/robomimic/backup_controller_insert.py @@ -25,7 +25,8 @@ class BackupSMWaitTime: LIFT_OBJECT = wp.constant(0.3) MIDPOINT = wp.constant(0.1) APPROACH_ABOVE_GOAL = wp.constant(0.1) - APPROACH_GOAL = wp.constant(6) + APPROACH_GOAL = wp.constant(0.1) + UNGRASP_OBJECT = wp.constant(1.0) class BackupSM: """States for the pick state machine.""" @@ -38,12 +39,27 @@ class BackupSM: MIDPOINT = wp.constant(5) APPROACH_ABOVE_GOAL = wp.constant(6) APPROACH_GOAL = wp.constant(7) - #UNGRASP_OBJECT = wp.constant(7) + UNGRASP_OBJECT = wp.constant(8) @wp.func def distance_below_threshold(current_pos: wp.vec3, desired_pos: wp.vec3, threshold: float) -> bool: return wp.length(current_pos - desired_pos) < threshold +@wp.func +def rotation_within_threshold(current_rot: wp.quat, desired_rot: wp.quat, threshold_rad: float) -> bool: + # Quaternion angular distance: angle = 2 * acos(|q1 · q2|) + # |dot| clamped to [0,1] to guard against floating point > 1 + dot = wp.abs( + current_rot[0] * desired_rot[0] + + current_rot[1] * desired_rot[1] + + current_rot[2] * desired_rot[2] + + current_rot[3] * desired_rot[3] + ) + dot = wp.min(dot, float(1.0)) + angle = float(2.0) * wp.acos(dot) + # print(f"angle : {angle}, threshold : {threshold_rad}") + return angle < threshold_rad + @wp.kernel def infer_state_machine( @@ -55,6 +71,7 @@ def infer_state_machine( current_object_pose: wp.array(dtype=wp.transform), # current object pose final_object_pose: wp.array(dtype=wp.transform), rest_ee_pose : wp.array(dtype=wp.transform), # rest position + goal_quat : wp.array(dtype=wp.transform), # goal rotation des_ee_pose: wp.array(dtype=wp.transform), # what we return gripper_state: wp.array(dtype=float), offset: wp.array(dtype=wp.transform), @@ -69,22 +86,26 @@ def infer_state_machine( state = sm_state[tid] # decide next state if state == BackupSM.REST: + # print("[SM State REST]") # First lift up to a safe height, then go to rest position current_ee_pos = wp.transform_get_translation(ee_pose[tid]) rest_pos = wp.transform_get_translation(rest_ee_pose[tid]) - rest_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + rest_rot = wp.transform_get_rotation(goal_quat[tid]) # If we're below safe height, first move up - safe_height = 0.35 # Safe z-height to avoid collisions + safe_height = 0.15 # Safe z-height to avoid collisions if current_ee_pos.z < safe_height: # Lift straight up first - lift_pos = wp.vec3(current_ee_pos.x, current_ee_pos.y, safe_height) + lift_pos = wp.vec3(current_ee_pos.x, current_ee_pos.y, 0.2) des_ee_pose[tid] = wp.transform(lift_pos, rest_rot) + #print("[SM REST doing safe ee lift]") else: # Already at safe height, move to rest position - des_ee_pose[tid] = rest_ee_pose[tid] + pose_pos = wp.transform_get_translation(rest_ee_pose[tid]) + pose_rot = wp.transform_get_rotation(goal_quat[tid]) + des_ee_pose[tid] = wp.transform(pose_pos, pose_rot) - gripper_state[tid] = GripperState.CLOSE + gripper_state[tid] = GripperState.OPEN # wait for a while if distance_below_threshold( wp.transform_get_translation(ee_pose[tid]), @@ -96,12 +117,14 @@ def infer_state_machine( # move to next state and reset wait time sm_state[tid] = BackupSM.APPROACH_ABOVE_OBJECT sm_wait_time[tid] = 0.0 + else: + print("[SM REST] wait timer ") elif state == BackupSM.APPROACH_ABOVE_OBJECT: # print("[SM] APPR above obj") pose_pos = wp.transform_get_translation(current_object_pose[tid]) - pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + pose_rot = wp.transform_get_rotation(goal_quat[tid]) # Go to a safe height ABOVE the object (z + 0.15m) with x offset - safe_above_pos = wp.vec3(pose_pos.x - 0.1, pose_pos.y, pose_pos.z + 0.15) + safe_above_pos = wp.vec3(pose_pos.x, pose_pos.y+0.1, pose_pos.z + 0.20) des_ee_pose[tid] = wp.transform(safe_above_pos, pose_rot) gripper_state[tid] = GripperState.OPEN # Open gripper in preparation if distance_below_threshold( @@ -119,16 +142,22 @@ def infer_state_machine( # print("[SM] approach object") pose_pos = wp.transform_get_translation(current_object_pose[tid]) - pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + pose_rot = wp.transform_get_rotation(goal_quat[tid]) # Apply offset in x-direction (5 cm = 0.05 m) - offset_pos = wp.vec3(pose_pos.x -0.1, pose_pos.y+0.02, pose_pos.z+0.02) #sample vial + 0.04 + offset_pos = wp.vec3(pose_pos.x, pose_pos.y+0.1, pose_pos.z) #sample vial + 0.04 des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) gripper_state[tid] = GripperState.OPEN - if distance_below_threshold( + pos_ok = distance_below_threshold( wp.transform_get_translation(ee_pose[tid]), wp.transform_get_translation(des_ee_pose[tid]), - position_threshold, - ): + 0.02, + ) + rot_ok = rotation_within_threshold( + wp.transform_get_rotation(ee_pose[tid]), + wp.transform_get_rotation(goal_quat[tid]), + float(0.05), # ~8.6 degrees tolerance + ) + if pos_ok and rot_ok: if sm_wait_time[tid] >= BackupSMWaitTime.APPROACH_OBJECT: # move to next state and reset wait time print("[SM_INFO] : Moving from APPR_OBJ to GRASP_OBJECT") @@ -137,9 +166,9 @@ def infer_state_machine( elif state == BackupSM.GRASP_OBJECT: # print("[SM] GRASP") pose_pos = wp.transform_get_translation(current_object_pose[tid]) - pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + pose_rot = wp.transform_get_rotation(goal_quat[tid]) # Stay at the approach position while closing gripper - offset_pos = wp.vec3(pose_pos.x - 0.1, pose_pos.y + 0.02, pose_pos.z + 0.02) + offset_pos = wp.vec3(pose_pos.x, pose_pos.y+0.1, pose_pos.z) des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) gripper_state[tid] = GripperState.CLOSE # wait for a while @@ -150,11 +179,12 @@ def infer_state_machine( sm_wait_time[tid] = 0.0 elif state == BackupSM.LIFT_OBJECT: - #print("[SM] LIFT") # uses starting position - pose_pos = wp.transform_get_translation(object_pose[tid]) - pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + #print("[SM] LIFT") # uses current object position (not start pose) + # pose_pos = wp.transform_get_translation(object_pose[tid]) + pose_pos = wp.transform_get_translation(current_object_pose[tid]) + pose_rot = wp.transform_get_rotation(goal_quat[tid]) # Apply offset in x-direction (5 cm = 0.05 m) - offset_pos = wp.vec3(pose_pos.x -0.1, pose_pos.y+0.02, pose_pos.z+0.2) #sample vial + 0.04 + offset_pos = wp.vec3(pose_pos.x, pose_pos.y+0.1, 0.25) #sample vial + 0.04 des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) gripper_state[tid] = GripperState.CLOSE # wait for a while @@ -172,10 +202,10 @@ def infer_state_machine( elif state == BackupSM.MIDPOINT: #print("[SM] MIDPOINT") pose_pos = wp.transform_get_translation(rest_ee_pose[tid]) - pose_rot = wp.transform_get_rotation(rest_ee_pose[tid]) + pose_rot = wp.transform_get_rotation(goal_quat[tid]) # Apply offset in x-direction (5 cm = 0.05 m) #offset_pos = wp.vec3(pose_pos.x -0.1, pose_pos.y+0.02, pose_pos.z+0.2) #sample vial + 0.04 - des_ee_pose[tid] = rest_ee_pose[tid] + des_ee_pose[tid] = wp.transform(pose_pos, pose_rot) gripper_state[tid] = GripperState.CLOSE # wait for a while if distance_below_threshold( @@ -191,25 +221,28 @@ def infer_state_machine( sm_wait_time[tid] = 0.0 elif state == BackupSM.APPROACH_ABOVE_GOAL: #print("[SM] Approach above goal") - offset_pos = wp.transform_get_translation(offset[tid]) - offset_rot = wp.transform_get_rotation(offset[tid]) - offset_pos = wp.vec3(offset_pos.x, offset_pos.y, offset_pos.z) # raise 25 cm - new_offset = wp.transform(offset_pos, offset_rot) - above_target_pose = wp.transform_multiply(new_offset, final_object_pose[tid]) + offset_pos = wp.transform_get_translation(final_object_pose[tid]) + offset_rot = wp.transform_get_rotation(goal_quat[tid]) + safe_above_pos = wp.vec3(offset_pos.x, offset_pos.y + 0.1, offset_pos.z + 0.25) + #offset_pos = wp.vec3(offset_pos.x, offset_pos.y, offset_pos.z) # raise 25 cm + above_target_pose = wp.transform(safe_above_pos, offset_rot) + #above_target_pose = wp.transform_multiply(new_offset, final_object_pose[tid]) # Blend time for smooth approach - APPROACH_BLEND_TIME = 0.4 # seconds, tune as needed - alpha = wp.clamp(sm_wait_time[tid] / APPROACH_BLEND_TIME, 0.0, 1.0) - # Interpolate position and rotation - current_pos = wp.transform_get_translation(ee_pose[tid]) - current_rot = wp.transform_get_rotation(ee_pose[tid]) + # APPROACH_BLEND_TIME = 0.4 # seconds, tune as needed + # alpha = wp.clamp(sm_wait_time[tid] / APPROACH_BLEND_TIME, 0.0, 1.0) + # # Interpolate position and rotation + # current_pos = wp.transform_get_translation(ee_pose[tid]) + # current_rot = wp.transform_get_rotation(ee_pose[tid]) - target_pos = wp.transform_get_translation(above_target_pose) - target_rot = wp.transform_get_rotation(above_target_pose) + # target_pos = wp.transform_get_translation(above_target_pose) + # target_rot = wp.transform_get_rotation(above_target_pose) - pos_interp = wp.lerp(current_pos, target_pos, alpha) - rot_interp = wp.quat_slerp(current_rot, target_rot, alpha) - # Set interpolated pose - des_ee_pose[tid] = wp.transform(pos_interp, rot_interp) + # pos_interp = wp.lerp(current_pos, target_pos, alpha) + # rot_interp = wp.quat_slerp(current_rot, target_rot, alpha) + # # Set interpolated pose + # des_ee_pose[tid] = wp.transform(pos_interp, rot_interp) + #safe_above_pos = wp.vec3(pose_pos.x - 0.1, pose_pos.y, pose_pos.z + 0.15) + des_ee_pose[tid] = above_target_pose gripper_state[tid] = GripperState.CLOSE # Evaluate readiness to transition if distance_below_threshold( @@ -224,23 +257,36 @@ def infer_state_machine( #print("[SM] approach goal") # print("[SM_INFO] : in state ungrasp") pose_pos = wp.transform_get_translation(final_object_pose[tid]) - pose_rot = wp.transform_get_rotation(final_object_pose[tid]) + pose_rot = wp.transform_get_rotation(goal_quat[tid]) # Apply offset in x-direction (5 cm = 0.05 m) - offset_pos = wp.vec3(pose_pos.x, pose_pos.y+0.1, pose_pos.z+0.02) + offset_pos = wp.vec3(pose_pos.x, pose_pos.y+0.1, pose_pos.z+0.07) des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) gripper_state[tid] = GripperState.CLOSE # wait for a while if distance_below_threshold( wp.transform_get_translation(ee_pose[tid]), wp.transform_get_translation(des_ee_pose[tid]), - 0.01, + 0.02, ): if sm_wait_time[tid] >= BackupSMWaitTime.APPROACH_GOAL: # move to next state and reset wait time - print("[SM_INFO] : Moving from apprach goal to rest") - sm_state[tid] = BackupSM.REST + print("[SM_INFO] : Moving from approach goal to ungrasp") + sm_state[tid] = BackupSM.UNGRASP_OBJECT sm_wait_time[tid] = 0.0 - + elif state == BackupSM.UNGRASP_OBJECT: + # print("[SM] GRASP") + pose_pos = wp.transform_get_translation(final_object_pose[tid]) + pose_rot = wp.transform_get_rotation(goal_quat[tid]) + # Stay at the approach position while closing gripper + offset_pos = wp.vec3(pose_pos.x , pose_pos.y + 0.1, pose_pos.z+0.01) + des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) + gripper_state[tid] = GripperState.OPEN + # wait for a while + if sm_wait_time[tid] >= BackupSMWaitTime.UNGRASP_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from UNGRASP_OBJECT to REST") + sm_state[tid] = BackupSM.REST + sm_wait_time[tid] = 0.0 # increment wait time sm_wait_time[tid] = sm_wait_time[tid] + dt[tid] @@ -251,7 +297,7 @@ def infer_state_machine( class BackupControllerInsertSM: - def __init__(self, dt: float, num_envs: int, device: torch.device | str = "cpu", position_threshold=0.02, offset = torch.tensor([[-0.1, 0, 0.1]], device='cuda:0')): + def __init__(self, dt: float, num_envs: int, device: torch.device | str = "cpu", position_threshold=0.02, offset = torch.tensor([[-0.1, 0, 0.1]], device='cuda:0'), goal_quat = torch.tensor([[0, 0, 0, 0,0,0,0]], device='cuda:0'), goal_pos = torch.tensor([[0, 0, 0, 0,0,0,0]], device='cuda:0')): """Initialize the state machine. Args: @@ -261,13 +307,14 @@ def __init__(self, dt: float, num_envs: int, device: torch.device | str = "cpu", """ ## debug - print(f'dt : {dt},num_envs {num_envs}, device {device}, pos_thresh {position_threshold} ') + print(f'dt : {dt},num_envs {num_envs}, \n device {device}, pos_thresh {position_threshold} \n goal_quat {goal_quat} \n goal_pos {goal_pos}') # save parameters self.dt = float(dt) self.num_envs = num_envs self.device = device self.position_threshold = position_threshold - + self.goal_quat = goal_quat + self.goal_pos = goal_pos # initialize state machine self.sm_dt = torch.full((self.num_envs,), self.dt, device=self.device) self.sm_state = torch.full((self.num_envs,), 0, dtype=torch.int32, device=self.device) @@ -278,6 +325,7 @@ def __init__(self, dt: float, num_envs: int, device: torch.device | str = "cpu", self.des_gripper_state = torch.full((self.num_envs,), 0.0, device=self.device) self.final_object_pose = torch.zeros((self.num_envs, 7), device=self.device) self.rest_pose = torch.zeros((self.num_envs, 7), device=self.device) + # approach above object offset self.offset = torch.zeros((self.num_envs, 7), device=self.device) @@ -294,6 +342,7 @@ def __init__(self, dt: float, num_envs: int, device: torch.device | str = "cpu", self.final_object_pose_wp = wp.from_torch(self.final_object_pose, wp.transform) self.rest_pose_wp = wp.from_torch(self.rest_pose, wp.transform) self.offset_wp = wp.from_torch(self.offset, wp.transform) + self.goal_quat_wp = wp.from_torch(self.goal_quat, wp.transform) ## For Debug self.debug_des_pose = torch.zeros((self.num_envs, 7), device=self.device) self.debug_cur_pose = torch.zeros((self.num_envs, 7), device=self.device) @@ -358,22 +407,25 @@ def compute(self, ee_pose: torch.Tensor, start_object_pose: torch.Tensor, curren # print("-" * 80) # print(f"Got given state {sm_state}") current_object_pose = self._ensure_7d(current_object_pose, self.device) - final_object_pose = self._ensure_7d(final_object_pose, self.device) + final_object_pose = self._ensure_7d(self.goal_pos, self.device) ee_pose = self._ensure_7d(ee_pose, self.device) start_object_pose = self._ensure_7d(start_object_pose, self.device) rest_pose = self._ensure_7d(rest_pose, self.device) + goal_quat = self._ensure_7d(self.goal_quat, self.device) # now safe to reorder ee_pose = ee_pose[:, [0, 1, 2, 4, 5, 6, 3]] start_object_pose = start_object_pose[:, [0, 1, 2, 4, 5, 6, 3]] current_object_pose = current_object_pose[:, [0, 1, 2, 4, 5, 6, 3]] final_object_pose = final_object_pose[:, [0, 1, 2, 4, 5, 6, 3]] rest_pose = rest_pose[:, [0, 1, 2, 4, 5, 6, 3]] + goal_quat = goal_quat[:, [0, 1, 2, 4, 5, 6, 3]] # convert to warp ee_pose_wp = wp.from_torch(ee_pose.contiguous(), wp.transform) start_object_pose_wp = wp.from_torch(start_object_pose.contiguous(), wp.transform) current_object_pose_wp = wp.from_torch(current_object_pose.contiguous(), wp.transform) final_object_pose_wp = wp.from_torch(final_object_pose.contiguous(), wp.transform) rest_pose_wp = wp.from_torch(rest_pose.contiguous(), wp.transform) + goal_quat_wp = wp.from_torch(goal_quat.contiguous(), wp.transform) # run state machine # get state from env - convert to warp again self.sm_state[env_num] = sm_state @@ -391,6 +443,7 @@ def compute(self, ee_pose: torch.Tensor, start_object_pose: torch.Tensor, curren current_object_pose_wp, final_object_pose_wp, rest_pose_wp, + goal_quat_wp, self.des_ee_pose_wp, self.des_gripper_state_wp, self.offset_wp, @@ -404,17 +457,21 @@ def compute(self, ee_pose: torch.Tensor, start_object_pose: torch.Tensor, curren des_debug = self.debug_des_pose.detach().cpu().numpy() cur_debug = self.debug_cur_pose.detach().cpu().numpy() debug_state = self.debug_state.detach().cpu().numpy() + goal_quat_debug = self.goal_quat.detach().cpu().numpy() for i in range(self.num_envs): des_pos, des_quat = des_debug[i][:3], des_debug[i][3:] cur_pos, cur_quat = cur_debug[i][:3], cur_debug[i][3:] + goal_quat = goal_quat_debug[i][3:] # compute Euclidean distance with numpy dist = np.linalg.norm(cur_pos - des_pos) # print(f"StateMachine debug state {debug_state}") - # print(f"[Env {i}]") - # print(f" Current -> pos: {cur_pos}, quat: {cur_quat}") - # print(f" Desired -> pos: {des_pos}, quat: {des_quat}") - #print(f" Euclidean distance: {dist:.4f}") - #print(f"returned state : {self.sm_state}") + # # print(f"[Env {i}]") + # print(f" Goal pose : {final_object_pose}") + # print(f" Current -> pos: {cur_pos}, quat: {cur_quat}") + # print(f" Desired -> pos: {des_pos}, quat: {des_quat}") + # print(f" Goal quat -> : {goal_quat}") + # print(f" Euclidean distance: {dist:.4f}") + # #print(f"returned state : {self.sm_state}") # convert transformations back to (w, x, y, z) des_ee_pose = self.des_ee_pose[:, [0, 1, 2, 6, 3, 4, 5]] # convert to torch diff --git a/scripts/imitation_learning/robomimic/backup_controller_insert_top.py b/scripts/imitation_learning/robomimic/backup_controller_insert_top.py new file mode 100644 index 000000000000..5a893b089255 --- /dev/null +++ b/scripts/imitation_learning/robomimic/backup_controller_insert_top.py @@ -0,0 +1,478 @@ +import warp as wp +import gymnasium as gym +import torch +from collections.abc import Sequence +import numpy as np + +# this is a backup state machine controller +# initialize warp +wp.init() + + +class GripperState: + """States for the gripper.""" + + OPEN = wp.constant(1.0) + CLOSE = wp.constant(-1.0) + +class BackupSMWaitTime: + """Additional wait times (in s) for states for before switching.""" + + REST = wp.constant(0.5) + APPROACH_ABOVE_OBJECT = wp.constant(0.3) + APPROACH_OBJECT = wp.constant(0.3) + GRASP_OBJECT = wp.constant(0.1) # 1 second wait + 0.3 for gripper to close + LIFT_OBJECT = wp.constant(0.3) + MIDPOINT = wp.constant(0.1) + APPROACH_ABOVE_GOAL = wp.constant(0.1) + APPROACH_GOAL = wp.constant(0.1) + UNGRASP_OBJECT = wp.constant(1.0) + +class BackupSM: + """States for the pick state machine.""" + + REST = wp.constant(0) + APPROACH_ABOVE_OBJECT = wp.constant(1) + APPROACH_OBJECT = wp.constant(2) + GRASP_OBJECT = wp.constant(3) + LIFT_OBJECT = wp.constant(4) + MIDPOINT = wp.constant(5) + APPROACH_ABOVE_GOAL = wp.constant(6) + APPROACH_GOAL = wp.constant(7) + UNGRASP_OBJECT = wp.constant(8) + +@wp.func +def distance_below_threshold(current_pos: wp.vec3, desired_pos: wp.vec3, threshold: float) -> bool: + return wp.length(current_pos - desired_pos) < threshold + +@wp.func +def rotation_within_threshold(current_rot: wp.quat, desired_rot: wp.quat, threshold_rad: float) -> bool: + # Quaternion angular distance: angle = 2 * acos(|q1 · q2|) + # |dot| clamped to [0,1] to guard against floating point > 1 + dot = wp.abs( + current_rot[0] * desired_rot[0] + + current_rot[1] * desired_rot[1] + + current_rot[2] * desired_rot[2] + + current_rot[3] * desired_rot[3] + ) + dot = wp.min(dot, float(1.0)) + angle = float(2.0) * wp.acos(dot) + # print(f"angle : {angle}, threshold : {threshold_rad}") + return angle < threshold_rad + + +@wp.kernel +def infer_state_machine( + dt: wp.array(dtype=float), + sm_state: wp.array(dtype=int), + sm_wait_time: wp.array(dtype=float), + ee_pose: wp.array(dtype=wp.transform), # current ee pose + object_pose: wp.array(dtype=wp.transform), # start object location + current_object_pose: wp.array(dtype=wp.transform), # current object pose + final_object_pose: wp.array(dtype=wp.transform), + rest_ee_pose : wp.array(dtype=wp.transform), # rest position + goal_quat : wp.array(dtype=wp.transform), # goal rotation + des_ee_pose: wp.array(dtype=wp.transform), # what we return + gripper_state: wp.array(dtype=float), + offset: wp.array(dtype=wp.transform), + position_threshold: float, + debug_des_pose: wp.array(dtype=wp.transform), # NEW + debug_cur_pose: wp.array(dtype=wp.transform), # NEW + debug_state: wp.array(dtype=int), # New +): + # retrieve thread id + tid = wp.tid() + # retrieve state machine state + state = sm_state[tid] + # decide next state + if state == BackupSM.REST: + # print("[SM State REST]") + # First lift up to a safe height, then go to rest position + current_ee_pos = wp.transform_get_translation(ee_pose[tid]) + rest_pos = wp.transform_get_translation(rest_ee_pose[tid]) + rest_rot = wp.transform_get_rotation(goal_quat[tid]) + + # If we're below safe height, first move up + safe_height = 0.15 # Safe z-height to avoid collisions + if current_ee_pos.z < safe_height: + # Lift straight up first + lift_pos = wp.vec3(current_ee_pos.x, current_ee_pos.y, 0.2) + des_ee_pose[tid] = wp.transform(lift_pos, rest_rot) + #print("[SM REST doing safe ee lift]") + else: + # Already at safe height, move to rest position + pose_pos = wp.transform_get_translation(rest_ee_pose[tid]) + pose_rot = wp.transform_get_rotation(goal_quat[tid]) + des_ee_pose[tid] = wp.transform(pose_pos, pose_rot) + + gripper_state[tid] = GripperState.OPEN + # wait for a while + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(rest_ee_pose[tid]), + 0.02, + ): + if sm_wait_time[tid] >= BackupSMWaitTime.REST: + print("[SM_INFO] : Moving from REST to APPROACH_ABOVE_OBJECT") + # move to next state and reset wait time + sm_state[tid] = BackupSM.APPROACH_ABOVE_OBJECT + sm_wait_time[tid] = 0.0 + else: + print("[SM REST] wait timer ") + elif state == BackupSM.APPROACH_ABOVE_OBJECT: + # print("[SM] APPR above obj") + pose_pos = wp.transform_get_translation(current_object_pose[tid]) + pose_rot = wp.transform_get_rotation(goal_quat[tid]) + # Go to a safe height ABOVE the object (z + 0.15m) with x offset + # left this in, but no offset due to new orientation + safe_above_pos = wp.vec3(pose_pos.x, pose_pos.y, pose_pos.z+0.15) + des_ee_pose[tid] = wp.transform(safe_above_pos, pose_rot) + gripper_state[tid] = GripperState.OPEN # Open gripper in preparation + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + position_threshold, + ): + # wait for a while + if sm_wait_time[tid] >= BackupSMWaitTime.APPROACH_ABOVE_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from APPR_ABOVE to APPROACH_OBJECT") + sm_state[tid] = BackupSM.APPROACH_OBJECT + sm_wait_time[tid] = 0.0 + elif state == BackupSM.APPROACH_OBJECT: + # print("[SM] approach object") + + pose_pos = wp.transform_get_translation(current_object_pose[tid]) + pose_rot = wp.transform_get_rotation(goal_quat[tid]) + # Apply offset in x-direction (5 cm = 0.05 m) + offset_pos = wp.vec3(pose_pos.x, pose_pos.y, pose_pos.z+0.12) #sample vial + 0.04 + des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) + gripper_state[tid] = GripperState.OPEN + pos_ok = distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + 0.01, + ) + + if pos_ok: + if sm_wait_time[tid] >= BackupSMWaitTime.APPROACH_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from APPR_OBJ to GRASP_OBJECT") + sm_state[tid] = BackupSM.GRASP_OBJECT + sm_wait_time[tid] = 0.0 + elif state == BackupSM.GRASP_OBJECT: + # print("[SM] GRASP") + pose_pos = wp.transform_get_translation(current_object_pose[tid]) + pose_rot = wp.transform_get_rotation(goal_quat[tid]) + # Stay at the approach position while closing gripper + offset_pos = wp.vec3(pose_pos.x, pose_pos.y, pose_pos.z+0.12) + des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if sm_wait_time[tid] >= BackupSMWaitTime.GRASP_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from GRSP to LIFT_OBJECT") + sm_state[tid] = BackupSM.LIFT_OBJECT + sm_wait_time[tid] = 0.0 + + elif state == BackupSM.LIFT_OBJECT: + #print("[SM] LIFT") # uses current object position (not start pose) + # pose_pos = wp.transform_get_translation(object_pose[tid]) + pose_pos = wp.transform_get_translation(current_object_pose[tid]) + pose_rot = wp.transform_get_rotation(goal_quat[tid]) + # Apply offset in x-direction (5 cm = 0.05 m) + offset_pos = wp.vec3(pose_pos.x, pose_pos.y, 0.25) #sample vial + 0.04 + des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + 0.03, + ): + # wait for a while + if sm_wait_time[tid] >= BackupSMWaitTime.LIFT_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from LIFT to MIDPOINT") + sm_state[tid] = BackupSM.MIDPOINT + sm_wait_time[tid] = 0.0 + elif state == BackupSM.MIDPOINT: + #print("[SM] MIDPOINT") + pose_pos = wp.transform_get_translation(rest_ee_pose[tid]) + pose_rot = wp.transform_get_rotation(goal_quat[tid]) + # Apply offset in x-direction (5 cm = 0.05 m) + #offset_pos = wp.vec3(pose_pos.x -0.1, pose_pos.y+0.02, pose_pos.z+0.2) #sample vial + 0.04 + des_ee_pose[tid] = wp.transform(pose_pos, pose_rot) + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + 0.03, + ): + # wait for a while + if sm_wait_time[tid] >= BackupSMWaitTime.LIFT_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from MID to APPR goal") + sm_state[tid] = BackupSM.APPROACH_ABOVE_GOAL + sm_wait_time[tid] = 0.0 + elif state == BackupSM.APPROACH_ABOVE_GOAL: + #print("[SM] Approach above goal") + offset_pos = wp.transform_get_translation(final_object_pose[tid]) + offset_rot = wp.transform_get_rotation(goal_quat[tid]) + safe_above_pos = wp.vec3(offset_pos.x, offset_pos.y, offset_pos.z+0.25) + #offset_pos = wp.vec3(offset_pos.x, offset_pos.y, offset_pos.z) # raise 25 cm + above_target_pose = wp.transform(safe_above_pos, offset_rot) + #above_target_pose = wp.transform_multiply(new_offset, final_object_pose[tid]) + # Blend time for smooth approach + # APPROACH_BLEND_TIME = 0.4 # seconds, tune as needed + # alpha = wp.clamp(sm_wait_time[tid] / APPROACH_BLEND_TIME, 0.0, 1.0) + # # Interpolate position and rotation + # current_pos = wp.transform_get_translation(ee_pose[tid]) + # current_rot = wp.transform_get_rotation(ee_pose[tid]) + + # target_pos = wp.transform_get_translation(above_target_pose) + # target_rot = wp.transform_get_rotation(above_target_pose) + + # pos_interp = wp.lerp(current_pos, target_pos, alpha) + # rot_interp = wp.quat_slerp(current_rot, target_rot, alpha) + # # Set interpolated pose + # des_ee_pose[tid] = wp.transform(pos_interp, rot_interp) + #safe_above_pos = wp.vec3(pose_pos.x - 0.1, pose_pos.y, pose_pos.z + 0.15) + des_ee_pose[tid] = above_target_pose + gripper_state[tid] = GripperState.CLOSE + # Evaluate readiness to transition + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + 0.01): + if sm_wait_time[tid] >= BackupSMWaitTime.APPROACH_ABOVE_GOAL: + print("[SM_INFO] : Moving from APPR_ABOVE to APPROACH_GOAL") + sm_state[tid] = BackupSM.APPROACH_GOAL + sm_wait_time[tid] = 0.0 + elif state == BackupSM.APPROACH_GOAL: + #print("[SM] approach goal") + # print("[SM_INFO] : in state ungrasp") + pose_pos = wp.transform_get_translation(final_object_pose[tid]) + pose_rot = wp.transform_get_rotation(goal_quat[tid]) + # Apply offset in x-direction (5 cm = 0.05 m) + # tune the z offset here ? + offset_pos = wp.vec3(pose_pos.x, pose_pos.y, pose_pos.z+0.15) + des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) + gripper_state[tid] = GripperState.CLOSE + # wait for a while + if distance_below_threshold( + wp.transform_get_translation(ee_pose[tid]), + wp.transform_get_translation(des_ee_pose[tid]), + 0.01, + ): + if sm_wait_time[tid] >= BackupSMWaitTime.APPROACH_GOAL: + # move to next state and reset wait time + print("[SM_INFO] : Moving from approach goal to ungrasp") + sm_state[tid] = BackupSM.UNGRASP_OBJECT + sm_wait_time[tid] = 0.0 + elif state == BackupSM.UNGRASP_OBJECT: + # print("[SM] GRASP") + pose_pos = wp.transform_get_translation(final_object_pose[tid]) + pose_rot = wp.transform_get_rotation(goal_quat[tid]) + # Stay at the approach position while closing gripper + offset_pos = wp.vec3(pose_pos.x , pose_pos.y, pose_pos.z+0.15) + des_ee_pose[tid] = wp.transform(offset_pos, pose_rot) + gripper_state[tid] = GripperState.OPEN + # wait for a while + if sm_wait_time[tid] >= BackupSMWaitTime.UNGRASP_OBJECT: + # move to next state and reset wait time + print("[SM_INFO] : Moving from UNGRASP_OBJECT to REST") + sm_state[tid] = BackupSM.REST + sm_wait_time[tid] = 0.0 + + # increment wait time + sm_wait_time[tid] = sm_wait_time[tid] + dt[tid] + debug_des_pose[tid] = des_ee_pose[tid] + debug_cur_pose[tid] = ee_pose[tid] + debug_state[tid] = sm_state[tid] + + + +class BackupControllerInsertTopSM: + def __init__(self, dt: float, num_envs: int, device: torch.device | str = "cpu", position_threshold=0.02, offset = torch.tensor([[-0.1, 0, 0.1]], device='cuda:0'), goal_quat = torch.tensor([[0, 0, 0, 0,0,0,0]], device='cuda:0'), goal_pos = torch.tensor([[0, 0, 0, 0,0,0,0]], device='cuda:0')): + """Initialize the state machine. + + Args: + dt: The environment time step. + num_envs: The number of environments to simulate. + device: The device to run the state machine on. + """ + ## debug + + print(f'dt : {dt},num_envs {num_envs}, \n device {device}, pos_thresh {position_threshold} \n goal_quat {goal_quat} \n goal_pos {goal_pos}') + # save parameters + self.dt = float(dt) + self.num_envs = num_envs + self.device = device + self.position_threshold = position_threshold + self.goal_quat = goal_quat + self.goal_pos = goal_pos + # initialize state machine + self.sm_dt = torch.full((self.num_envs,), self.dt, device=self.device) + self.sm_state = torch.full((self.num_envs,), 0, dtype=torch.int32, device=self.device) + self.sm_wait_time = torch.zeros((self.num_envs,), device=self.device) + + # desired state + self.des_ee_pose = torch.zeros((self.num_envs, 7), device=self.device) + self.des_gripper_state = torch.full((self.num_envs,), 0.0, device=self.device) + self.final_object_pose = torch.zeros((self.num_envs, 7), device=self.device) + self.rest_pose = torch.zeros((self.num_envs, 7), device=self.device) + + # approach above object offset + + self.offset = torch.zeros((self.num_envs, 7), device=self.device) + self.offset[:, 2] = 0.2 + self.offset[:,0] = -0.1 + self.offset[:, -1] = 1.0 # warp expects quaternion as (x, y, z, w) + + # convert to warp + self.sm_dt_wp = wp.from_torch(self.sm_dt, wp.float32) + self.sm_state_wp = wp.from_torch(self.sm_state, wp.int32) + self.sm_wait_time_wp = wp.from_torch(self.sm_wait_time, wp.float32) + self.des_ee_pose_wp = wp.from_torch(self.des_ee_pose, wp.transform) + self.des_gripper_state_wp = wp.from_torch(self.des_gripper_state, wp.float32) + self.final_object_pose_wp = wp.from_torch(self.final_object_pose, wp.transform) + self.rest_pose_wp = wp.from_torch(self.rest_pose, wp.transform) + self.offset_wp = wp.from_torch(self.offset, wp.transform) + self.goal_quat_wp = wp.from_torch(self.goal_quat, wp.transform) + ## For Debug + self.debug_des_pose = torch.zeros((self.num_envs, 7), device=self.device) + self.debug_cur_pose = torch.zeros((self.num_envs, 7), device=self.device) + self.debug_state = torch.full((self.num_envs,), 0, dtype=torch.int32, device=self.device) + + self.debug_des_pose_wp = wp.from_torch(self.debug_des_pose, wp.transform) + self.debug_cur_pose_wp = wp.from_torch(self.debug_cur_pose, wp.transform) + self.debug_state_wp = wp.from_torch(self.debug_state, wp.int32) + + def reset_idx(self, env_ids: Sequence[int] = None): + """Reset the state machine.""" + print(f'[SM RESET]') + if env_ids is None: + env_ids = slice(None) + self.sm_state[env_ids] = 0 + self.sm_wait_time[env_ids] = 0.0 + + def _ensure_7d(self,tensor: torch.Tensor, device): + # accepts (N,3) -> returns (N,7) with identity quaternion + if tensor.ndim != 2: + raise ValueError(f"expected 2D tensor, got shape {tensor.shape}") + if tensor.shape[1] == 7: + return tensor + if tensor.shape[1] == 3: + quat = torch.tensor([0.0,0.0,0.0,1.0], device=device, dtype=tensor.dtype) + quat = quat.unsqueeze(0).expand(tensor.shape[0], -1) + return torch.cat([tensor, quat], dim=-1) + raise ValueError(f"pose must have 3 or 7 elements, got {tensor.shape[1]}") + + + def compute(self, ee_pose: torch.Tensor, start_object_pose: torch.Tensor, current_object_pose: torch.Tensor, final_object_pose: torch.Tensor, rest_pose: torch.Tensor, sm_state: int =0, env_num = 0) -> torch.Tensor: + """Compute the desired state of the robot's end-effector and the gripper.""" + # convert all transformations from (w, x, y, z) to (x, y, z, w) + # print(f'[SM DEBUG] Doing State Comp') + # print(f'[SM DEBUG] ee_pose {ee_pose}') + # print(f'[SM DEBUG] object pose {object_pose}') + # print(f'[SM DEBUG] desired pose {des_object_pose}') + # print(f'[SM DEBUG] final pose {final_object_pose}') + # print(f'[SM DEBUG] rest pose {rest_pose}') + # before reordering + # print("\n[BackupControllerSM::compute] called with inputs:") + # print(f" sm_state: {sm_state}, env_num: {env_num}") + + # def short_tensor(t, name): + # if t is None: + # print(f" {name}: None") + # elif not isinstance(t, torch.Tensor): + # print(f" {name}: {t} (type={type(t)})") + # else: + # shape = tuple(t.shape) + # device = t.device + # dtype = t.dtype + # # show first few elements flattened + # vals = t.flatten()[:6].detach().cpu().numpy() + # print(f" {name}: shape={shape}, device={device}, dtype={dtype}, values={vals}") + + # short_tensor(ee_pose, "ee_pose") + # short_tensor(object_pose, "object_pose") + # short_tensor(des_object_pose, "des_object_pose") + # short_tensor(final_object_pose, "final_object_pose") + # short_tensor(rest_pose, "rest_pose") + # print("-" * 80) + # print(f"Got given state {sm_state}") + current_object_pose = self._ensure_7d(current_object_pose, self.device) + final_object_pose = self._ensure_7d(self.goal_pos, self.device) + ee_pose = self._ensure_7d(ee_pose, self.device) + start_object_pose = self._ensure_7d(start_object_pose, self.device) + rest_pose = self._ensure_7d(rest_pose, self.device) + goal_quat = self._ensure_7d(self.goal_quat, self.device) + # now safe to reorder + ee_pose = ee_pose[:, [0, 1, 2, 4, 5, 6, 3]] + start_object_pose = start_object_pose[:, [0, 1, 2, 4, 5, 6, 3]] + current_object_pose = current_object_pose[:, [0, 1, 2, 4, 5, 6, 3]] + final_object_pose = final_object_pose[:, [0, 1, 2, 4, 5, 6, 3]] + rest_pose = rest_pose[:, [0, 1, 2, 4, 5, 6, 3]] + goal_quat = goal_quat[:, [0, 1, 2, 4, 5, 6, 3]] + # convert to warp + ee_pose_wp = wp.from_torch(ee_pose.contiguous(), wp.transform) + start_object_pose_wp = wp.from_torch(start_object_pose.contiguous(), wp.transform) + current_object_pose_wp = wp.from_torch(current_object_pose.contiguous(), wp.transform) + final_object_pose_wp = wp.from_torch(final_object_pose.contiguous(), wp.transform) + rest_pose_wp = wp.from_torch(rest_pose.contiguous(), wp.transform) + goal_quat_wp = wp.from_torch(goal_quat.contiguous(), wp.transform) + # run state machine + # get state from env - convert to warp again + self.sm_state[env_num] = sm_state + self.sm_state_wp = wp.from_torch(self.sm_state, wp.int32) + # launch + wp.launch( + kernel=infer_state_machine, + dim=self.num_envs, + inputs=[ + self.sm_dt_wp, + self.sm_state_wp, + self.sm_wait_time_wp, + ee_pose_wp, + start_object_pose_wp, + current_object_pose_wp, + final_object_pose_wp, + rest_pose_wp, + goal_quat_wp, + self.des_ee_pose_wp, + self.des_gripper_state_wp, + self.offset_wp, + self.position_threshold, + self.debug_des_pose_wp, + self.debug_cur_pose_wp, + self.debug_state_wp, + ], + device=self.device, + ) + des_debug = self.debug_des_pose.detach().cpu().numpy() + cur_debug = self.debug_cur_pose.detach().cpu().numpy() + debug_state = self.debug_state.detach().cpu().numpy() + goal_quat_debug = self.goal_quat.detach().cpu().numpy() + for i in range(self.num_envs): + des_pos, des_quat = des_debug[i][:3], des_debug[i][3:] + cur_pos, cur_quat = cur_debug[i][:3], cur_debug[i][3:] + goal_quat = goal_quat_debug[i][3:] + # compute Euclidean distance with numpy + dist = np.linalg.norm(cur_pos - des_pos) + print(f"StateMachine debug state {debug_state}") + # print(f"[Env {i}]") + print(f" Goal pose : {final_object_pose}") + print(f" Current -> pos: {cur_pos}, quat: {cur_quat}") + print(f" Desired -> pos: {des_pos}, quat: {des_quat}") + print(f" Goal quat -> : {goal_quat}") + print(f" Euclidean distance: {dist:.4f}") + # #print(f"returned state : {self.sm_state}") + # convert transformations back to (w, x, y, z) + des_ee_pose = self.des_ee_pose[:, [0, 1, 2, 6, 3, 4, 5]] + # convert to torch + #print(f'[SM DEBUG] new desired poition : {des_ee_pose}, gripper state {self.des_gripper_state}') + return des_ee_pose , self.des_gripper_state, self.sm_state.clone().item() + #return torch.cat([des_ee_pose, self.des_gripper_state.unsqueeze(-1)], dim=-1) \ No newline at end of file diff --git a/scripts/imitation_learning/robomimic/play_controller.py b/scripts/imitation_learning/robomimic/play_controller.py index 166b1d518540..88454818a962 100644 --- a/scripts/imitation_learning/robomimic/play_controller.py +++ b/scripts/imitation_learning/robomimic/play_controller.py @@ -118,7 +118,7 @@ def rollout_ensemble(env, success_term, horizon, device): ### SET USE RECOVERY TO FALSE #### # Set up recovery controller - backup_controller = BackupControllerSM(0.01*2, num_envs,'cuda' , 0.01) + backup_controller = BackupControllerInsertSM(0.01*2, num_envs,'cuda' , 0.01) backup_controller.reset_idx() # none so reset all robot = env.unwrapped.scene["robot"] diff --git a/scripts/imitation_learning/robomimic/play_ensemble_v05.py b/scripts/imitation_learning/robomimic/play_ensemble_v05.py index c10cfccf1be6..e3897f85f443 100644 --- a/scripts/imitation_learning/robomimic/play_ensemble_v05.py +++ b/scripts/imitation_learning/robomimic/play_ensemble_v05.py @@ -344,7 +344,7 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, max_recovery_steps = 500 # Safety limit for recovery mode ##### CONFIG RECOVERY CONTROLLER #### - backup_controller = BackupController(env, device, tasktype="lift") + backup_controller = BackupController(env, device, tasktype="place") state_guess = 0 last_state = 0 recovery_mode = False @@ -593,52 +593,94 @@ def main(): # stack_cube_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/stack_cube_ensemble.txt') #pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/ensembles.txt') - #pick_place_ensemble = load_ensemble(device, ensemble_path='docs/place/low/Dev-IK-Rel-Place-v0/best_models/best_model_paths.txt') + pick_place_ensemble = load_ensemble(device, ensemble_path='docs/place/low/Dev-IK-Rel-Place-v0/best_models/best_model_paths.txt') # pick_place_ensemble_30 = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/pick_place_ensemble_30_paths.txt') # pick_place_ensemble = load_ensemble(device, ensemble_path='docs/lift/Dev-IK-Rel-v1/best_models/best_model_paths.txt') - pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/insert_paths.txt') + # pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/insert_paths.txt') + #pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/inser_top_paths.txt') # Lets set these to the 0.99 confidence parameters = { 'beaker_lift' :{ 0 : { - "confidence_level": 0.0002104382060000009, + "confidence_level": 5.373953000000001e-05, "window_size": 10, "max_peaks": 8 }, 1 : { - "confidence_level": 0.00020459246700000051, + "confidence_level": 5.378578e-05, "window_size": 10, "max_peaks": 8 }, 2 : { - "confidence_level": 0.00023551160300000013, + "confidence_level": 6.186318e-05, "window_size": 10, "max_peaks": 8 }, 3 : { - "confidence_level": 0.0003701742110000007, + "confidence_level": 7.819939000000004e-05, "window_size": 10, "max_peaks": 8 }, 4 : { - "confidence_level": 0.0002939644620000011, + "confidence_level": 0.00012073204000000005, "window_size": 10, "max_peaks": 8 }, 5 : { - "confidence_level": 0.0006407762280000002, + "confidence_level": 8.867226000000004e-05, "window_size": 10, "max_peaks": 8 }, 6 : { - "confidence_level": 0.49527883679, + "confidence_level": 1.57e-08, "window_size": 10, "max_peaks": 8 }, + + }, + 'vial_insert': { + 0 : { + "confidence_level": 0.00016269120400000059, + "window_size": 9, + "max_peaks": 8 + }, + 1 : { + "confidence_level": 0.000173801014, + "window_size": 9, + "max_peaks": 8 + }, + 2 : { + "confidence_level": 0.00018948240900000074, + "window_size": 9, + "max_peaks": 8 + }, + 3 : { + "confidence_level": 0.0007136359360000004, + "window_size": 9, + "max_peaks": 8 + }, + 4 : { + "confidence_level": 0.00015286798600000018, + "window_size": 9, + "max_peaks": 8 + }, + 5 : { + "confidence_level": 0.00047967177600000013, + "window_size": 9, + "max_peaks": 8 + }, + 6 : { + "confidence_level": 0.26672138987099997, + "window_size": 9, + "max_peaks": 8 + }, + + + } } diff --git a/scripts/imitation_learning/robomimic/play_with_backup.py b/scripts/imitation_learning/robomimic/play_with_backup.py index ef7c15bbaf15..db88bb2cf943 100644 --- a/scripts/imitation_learning/robomimic/play_with_backup.py +++ b/scripts/imitation_learning/robomimic/play_with_backup.py @@ -118,7 +118,7 @@ def rollout_ensemble(env, success_term, horizon, device): # Set up recovery controller - backup_controller = BackupController(env, device, tasktype="lift") + backup_controller = BackupController(env, device, tasktype="insert_top") state_guess = 0 for i in range(horizon): diff --git a/scripts/imitation_learning/robomimic/switching_logic_algorithm.tex b/scripts/imitation_learning/robomimic/switching_logic_algorithm.tex new file mode 100644 index 000000000000..198c0267ee15 --- /dev/null +++ b/scripts/imitation_learning/robomimic/switching_logic_algorithm.tex @@ -0,0 +1,62 @@ +\begin{algorithm}[t] +\caption{Uncertainty-Based Switching Logic for Safe Robot Manipulation} +\label{alg:switching_logic} +\begin{algorithmic}[1] +\Procedure{SwitchingLogic}{$\text{uncertainties}, \text{step}, \text{parameters}$} + \State \textit{// Initialization} + \State $\text{counts} \gets 0$ + \State $\text{triggered} \gets \text{False}$ + + \State \textit{// Iterate over each joint/action dimension} + \For{$j = 0$ \textbf{to} $|\text{uncertainties}| - 1$} + \State $\text{unc} \gets \text{uncertainties}[j]$ + \State $\text{traj}[j][\text{step}] \gets \text{unc}$ + + \State \textit{// Check if sufficient history exists} + \If{$\text{step} > \text{parameters}[j][\text{window\_size}]$} + \State \textit{// Extract sliding window of recent uncertainties} + \State $\text{window\_start} \gets \text{step} - \text{parameters}[j][\text{window\_size}]$ + \State $\text{test\_window} \gets \text{traj}[j][\text{window\_start} : \text{step}]$ + + \State \textit{// Count peaks exceeding confidence threshold} + \State $\text{peaks} \gets 0$ + \For{$x \in \text{test\_window}$} + \If{$x \geq \text{parameters}[j][\text{confidence\_level}]$} + \State $\text{peaks} \gets \text{peaks} + 1$ + \EndIf + \EndFor + + \State \textit{// Check if peaks exceed maximum allowed} + \If{$\text{peaks} \geq \text{parameters}[j][\text{max\_peaks}]$} + \State $\text{counts} \gets \text{counts} + 1$ + \EndIf + \EndIf + \EndFor + + \State \textit{// Trigger safety mechanism if multiple joints show high uncertainty} + \If{$\text{counts} > 2$} + \If{\textbf{not} $\text{triggered}$} + \State $\text{triggered} \gets \text{True}$ + \State \textbf{print} ``Uncertainty triggered safety condition'' + \EndIf + \State \Return \textbf{True} \Comment{Switch to backup controller} + \EndIf + + \State \Return \textbf{False} \Comment{Continue with learned policy} +\EndProcedure + +\State + +\Procedure{HalfwayCheck}{$\text{step}, \text{grasp\_state}, \text{horizon}$} + \State \textit{// Verify subtask completion at midpoint} + \If{$\text{step} = \lfloor \text{horizon} / 2 \rfloor$ \textbf{and not} $\text{grasp\_state}$} + \If{\textbf{not} $\text{triggered}$} + \State \textbf{print} ``Halfway point reached without subtask completion'' + \State $\text{triggered} \gets \text{True}$ + \EndIf + \State \Return \textbf{True} \Comment{Activate recovery} + \EndIf + \State \Return \textbf{False} +\EndProcedure +\end{algorithmic} +\end{algorithm} diff --git a/scripts/imitation_learning/robomimic/train_ensemble.py b/scripts/imitation_learning/robomimic/train_ensemble.py index 9a9f6bb5d954..9e1e5893059a 100644 --- a/scripts/imitation_learning/robomimic/train_ensemble.py +++ b/scripts/imitation_learning/robomimic/train_ensemble.py @@ -177,9 +177,9 @@ def train(config: Config, device: str, log_dirs: list[str], ckpt_dirs: list[str] env_meta = FileUtils.get_env_metadata_from_dataset(dataset_path=config.train.data) print(f"config.train.data: {config.train.data}") with config.values_unlocked(): - config.train.data = [{'path': 'docs/place/glassware_insert_demos_generated_split.hdf5'}] + config.train.data = [{'path': 'docs/insert/glassware_insert_demos_top_generated_split.hdf5'}] ####### HARD CODED MAKE SURE THIS GETS UPDATED ######### - config_list= [{'path': 'docs/place/glassware_insert_demos_generated_split.hdf5'}] + config_list= [{'path': 'docs/insert/glassware_insert_demos_top_generated_split.hdf5'}] print(f"config.train.data: {config.train.data}") # print(f"config_list: {config_list}") # if isinstance(config.train.data, str): diff --git a/scripts/tools/record_SM_demos.py b/scripts/tools/record_SM_demos.py new file mode 100644 index 000000000000..b6de2f4fe953 --- /dev/null +++ b/scripts/tools/record_SM_demos.py @@ -0,0 +1,364 @@ +# Copyright (c) 2024-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +""" +Script to record demonstrations with Isaac Lab environments using human teleoperation. + +This script allows users to record demonstrations operated by human teleoperation for a specified task. +The recorded demonstrations are stored as episodes in a hdf5 file. Users can specify the task, teleoperation +device, dataset directory, and environment stepping rate through command-line arguments. + +required arguments: + --task Name of the task. + +optional arguments: + -h, --help Show this help message and exit + --teleop_device Device for interacting with environment. (default: keyboard) + --dataset_file File path to export recorded demos. (default: "./datasets/dataset.hdf5") + --step_hz Environment stepping rate in Hz. (default: 30) + --num_demos Number of demonstrations to record. (default: 0) + --num_success_steps Number of continuous steps with task success for concluding a demo as successful. (default: 10) +""" + +"""Launch Isaac Sim Simulator first.""" + +# Standard library imports +import argparse +import contextlib + +# Third-party imports +import gymnasium as gym +import numpy as np +import os +import time +import torch + +# Isaac Lab AppLauncher +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Record demonstrations for Isaac Lab environments.") +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument("--teleop_device", type=str, default="keyboard", help="Device for interacting with environment.") +parser.add_argument( + "--dataset_file", type=str, default="./datasets/dataset.hdf5", help="File path to export recorded demos." +) +parser.add_argument("--step_hz", type=int, default=30, help="Environment stepping rate in Hz.") +parser.add_argument( + "--num_demos", type=int, default=0, help="Number of demonstrations to record. Set to 0 for infinite." +) +parser.add_argument( + "--num_success_steps", + type=int, + default=10, + help="Number of continuous steps with task success for concluding a demo as successful. Default is 10.", +) +parser.add_argument("--seed", type=int, default=101, help="Random seed.") +parser.add_argument( + "--enable_pinocchio", + action="store_true", + default=False, + help="Enable Pinocchio.", +) +parser.add_argument( + "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations." +) +parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.") + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +app_launcher_args = vars(args_cli) + +if args_cli.enable_pinocchio: + # Import pinocchio before AppLauncher to force the use of the version installed by IsaacLab and not the one installed by Isaac Sim + # pinocchio is required by the Pink IK controllers and the GR1T2 retargeter + import pinocchio # noqa: F401 + +# launch the simulator +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +# Omniverse logger +import omni.log + +if not args_cli.headless: + import omni.ui as ui + from isaaclab_mimic.ui.instruction_display import InstructionDisplay, show_subtask_instructions + from isaaclab.envs.ui import EmptyWindow + + +import isaaclab_mimic.envs # noqa: F401 +from isaaclab_mimic.ui.instruction_display import InstructionDisplay, show_subtask_instructions + +if args_cli.enable_pinocchio: + from isaaclab.devices.openxr.retargeters.humanoid.fourier.gr1t2_retargeter import GR1T2Retargeter + import isaaclab_tasks.manager_based.manipulation.pick_place # noqa: F401 + +from isaaclab.devices.openxr.retargeters.manipulator import GripperRetargeter, Se3AbsRetargeter, Se3RelRetargeter +from isaaclab.envs.mdp.recorders.recorders_cfg import ActionStateRecorderManagerCfg +from isaaclab.envs.ui import EmptyWindow +from isaaclab.managers import DatasetExportMode + +import isaaclab_tasks # noqa: F401 +from isaaclab_tasks.utils.parse_cfg import parse_env_cfg +from scripts.imitation_learning.robomimic.backup_controller_handler import BackupController + + +#rom isaaclab_tasks.manager_based.manipulation.lift.lift_env_cfg import LiftEnvCfg +#from scripts.environments.state_machine.stack_lab_sm import PickAndLiftSm +#from scripts.environments.state_machine.weigh_lab_sm import PickAndLiftSm +#from scripts.environments.state_machine.pour_lab_sm import PickAndLiftSm +#from scripts.environments.state_machine.stack_weigh_lab_sm import PickAndLiftSm + + +class RateLimiter: + """Convenience class for enforcing rates in loops.""" + + def __init__(self, hz: int): + """Initialize a RateLimiter with specified frequency. + + Args: + hz: Frequency to enforce in Hertz. + """ + self.hz = hz + self.last_time = time.time() + self.sleep_duration = 1.0 / hz + self.render_period = min(0.033, self.sleep_duration) + + def sleep(self, env: gym.Env): + """Attempt to sleep at the specified rate in hz. + + Args: + env: Environment to render during sleep periods. + """ + next_wakeup_time = self.last_time + self.sleep_duration + while time.time() < next_wakeup_time: + time.sleep(self.render_period) + if not args_cli.headless: + env.sim.render() + + self.last_time = self.last_time + self.sleep_duration + + # detect time jumping forwards (e.g. loop is too slow) + if self.last_time < time.time(): + while self.last_time < time.time(): + self.last_time += self.sleep_duration + +def init_state_machine(env, device): + """Create PickAndLiftSm once and capture initial TCP orientation.""" + + backup_controller = BackupController(env, device, tasktype="insert_top") + state_guess = 0 + + # Read current ee frame and capture a fixed orientation (per env) + + + return backup_controller + +def state_machine_step(env, backup_controller, fixed_tcp_rot): + + action, state_guess = backup_controller.get_controller_action(state_guess, 0) + """Compute a single-step action from the persistent state machine (does NOT call env.step).""" + + return action + +def reset_env_and_sm(env, backup_controller, reset_indices=None): + """Reset specified environments (or all if None) along with the state machine. + + Args: + env: The environment instance + pick_sm: The state machine + reset_indices: Optional list/1D tensor of env indices to reset. If None, resets all. + Returns: + fixed_tcp_rot: (num_envs, 4) tensor of fixed TCP rotations + actions: action buffer tensor + """ + if reset_indices is None: + # full reset + env.sim.reset() + env.reset() + reset_indices = torch.arange(env.unwrapped.num_envs, device=env.unwrapped.device) + else: + # partial reset + env.reset(reset_indices) + + # reset state machine for these envs + backup_controller.reset() + + # get new tcp rotation for *all* envs (keep shape consistent) + # ee_frame = env.unwrapped.scene["ee_frame"] + # fixed_tcp_rot = ee_frame.data.target_quat_w[..., 0, :].clone() + + # reset action buffer (neutral gripper open) + actions = torch.zeros(env.unwrapped.action_space.shape, device=env.unwrapped.device) + + return actions + +def main(): + """Collect demonstrations from the environment using teleop interfaces.""" + + if args_cli.headless: + rate_limiter = None + else: + rate_limiter = RateLimiter(args_cli.step_hz) + + # prepare dataset directory + output_dir = os.path.dirname(args_cli.dataset_file) + output_file_name = os.path.splitext(os.path.basename(args_cli.dataset_file))[0] + os.makedirs(output_dir, exist_ok=True) + + num_envs = 1 + success_step_counts = [0 for _ in range(num_envs)] + + env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=1, use_fabric=not args_cli.disable_fabric) + success_term = env_cfg.terminations.success + env_cfg.terminations.success = None + # success_term = None + # if hasattr(env_cfg.terminations, "success_term"): + # success_term = env_cfg.terminations.success_term + # env_cfg.terminations.success_term = None + # else: + # omni.log.warn("No success termination term found — cannot mark demos as successful.") + + env_cfg.recorders: ActionStateRecorderManagerCfg = ActionStateRecorderManagerCfg() + env_cfg.recorders.dataset_export_dir_path = output_dir + env_cfg.recorders.dataset_filename = output_file_name + env_cfg.recorders.dataset_export_mode = DatasetExportMode.EXPORT_SUCCEEDED_ONLY + + env = gym.make(args_cli.task, cfg=env_cfg).unwrapped + # Set seed9 + torch.manual_seed(args_cli.seed) + env.seed(args_cli.seed) + # control flags + should_reset_recording_instance = False + running_recording_instance = True + + def reset_recording_instance(): + nonlocal should_reset_recording_instance + should_reset_recording_instance = True + + def start_recording_instance(): + nonlocal running_recording_instance + running_recording_instance = True + + def stop_recording_instance(): + nonlocal running_recording_instance + running_recording_instance = False + + # reset everything at start + env.sim.reset() + env.reset() + + current_recorded_demo_count = 0 + label_text = f"Recorded {current_recorded_demo_count} successful demonstrations." + + if not args_cli.headless: + instruction_display = InstructionDisplay(args_cli.teleop_device) + window = EmptyWindow(env, "Instruction") + with window.ui_window_elements["main_vstack"]: + demo_label = ui.Label(label_text) + subtask_label = ui.Label("") + instruction_display.set_labels(subtask_label, demo_label) + else: + demo_label = None + subtask_label = None + print(label_text) + + # init state machine + reset all envs + pick_sm= init_state_machine(env, args_cli.device) + actions = reset_env_and_sm(env, pick_sm) + state_guess=0 + while simulation_app.is_running(): + if running_recording_instance: + # standard Gymnasium step return + actions, state_guess = pick_sm.get_controller_action(state_guess, 0) + obs, rewards, dones, infos, _ = env.step(actions) + + # compute next actions + #action, state_guess = pick_sm.get_controller_action(state_guess, 0) + if dones.any(): + done_mask = dones.detach().cpu().numpy().astype(bool) if isinstance(dones, torch.Tensor) else np.asarray(dones, dtype=bool) + + # success mask from env infos (matches training!) + if "success_term" in infos: + success_mask = infos["success_term"].detach().cpu().numpy().astype(bool) + else: + success_mask = np.zeros(env.unwrapped.num_envs, dtype=bool) + + exported_indices = [] + for idx in range(env.unwrapped.num_envs): + if not done_mask[idx]: + continue # only process finished envs + + if success_mask[idx]: + success_step_counts[idx] += 1 + print(f"[INFO] Env {idx} success streak: {success_step_counts[idx]}") + else: + if success_step_counts[idx] != 0: + print(f"[INFO] Env {idx} failed, resetting streak") + success_step_counts[idx] = 0 + + # reached threshold → export demo + if success_step_counts[idx] >= args_cli.num_success_steps: + print(f"[INFO] Exporting successful demo for env {idx}") + env.recorder_manager.record_pre_reset([idx], force_export_or_skip=False) + env.recorder_manager.set_success_to_episodes( + [idx], + torch.tensor([[True]], dtype=torch.bool, device=env.device), + ) + env.recorder_manager.export_episodes([idx]) + exported_indices.append(idx) + success_step_counts[idx] = 0 + + if exported_indices: + print(f"[INFO] Exported env indices: {exported_indices}") + pick_sm.reset() + fixed_tcp_rot, actions = reset_env_and_sm( + env, pick_sm, torch.tensor(exported_indices, device=env.device) + ) + else: + pick_sm.reset() + reset_indices = [idx for idx in range(env.unwrapped.num_envs) if done_mask[idx]] + if reset_indices: + print(f"[INFO] Resetting env indices: {reset_indices}") + fixed_tcp_rot, actions = reset_env_and_sm( + env, pick_sm, torch.tensor(reset_indices, device=env.device) + ) + for idx in reset_indices: + success_step_counts[idx] = 0 + + else: + if not args_cli.headless: + env.sim.render() + + # update demo count label + if env.recorder_manager.exported_successful_episode_count > current_recorded_demo_count: + current_recorded_demo_count = env.recorder_manager.exported_successful_episode_count + label_text = f"Recorded {current_recorded_demo_count} successful demonstrations." + print(label_text) + + # stop when enough demos are recorded + if args_cli.num_demos > 0 and env.recorder_manager.exported_successful_episode_count >= args_cli.num_demos: + print(f"All {args_cli.num_demos} demonstrations recorded. Exiting the app.") + break + + if env.sim.is_stopped(): + break + + if rate_limiter: + rate_limiter.sleep(env) + + + env.close() + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() \ No newline at end of file diff --git a/scripts/tools/start_discovery_server.sh b/scripts/tools/start_discovery_server.sh new file mode 100644 index 000000000000..d6d74339f624 --- /dev/null +++ b/scripts/tools/start_discovery_server.sh @@ -0,0 +1,32 @@ +#!/bin/bash + +# This script starts a ROS 2 Discovery Server. +# Use this when communicating over a VPN where multicast is blocked. + +# Get the IP address of the machine on the VPN (e.g., tailscale0, tun0) +# You can also manually set this if the auto-detection fails. +VPN_INTERFACE=$(ip addr | grep -e "tailscale" -e "tun" | awk '{print $NF}' | head -n 1) + +if [ -z "$VPN_INTERFACE" ]; then + echo "[WARNING] No VPN interface (tailscale, tun) detected. Falling back to eth0." + VPN_INTERFACE="eth0" +fi + +IP_ADDR=$(ip addr show $VPN_INTERFACE | grep "inet " | awk '{print $2}' | cut -d/ -f1) + +if [ -z "$IP_ADDR" ]; then + echo "[ERROR] Could not detect IP address for interface $VPN_INTERFACE." + exit 1 +fi + +PORT=11811 + +echo "[INFO] Starting ROS 2 Discovery Server on $IP_ADDR:$PORT (Interface: $VPN_INTERFACE)" +echo "[INFO] To connect other machines, run: export ROS_DISCOVERY_SERVER=$IP_ADDR:$PORT" + +# Source ROS 2 if not already sourced +if [ -f "/opt/ros/humble/setup.bash" ]; then + source /opt/ros/humble/setup.bash +fi + +fastdds discovery --server-id 0 --ip-address $IP_ADDR --port $PORT diff --git a/source/isaaclab/isaaclab/envs/mdp/observations.py b/source/isaaclab/isaaclab/envs/mdp/observations.py index ac502521aae0..f3daee777aa5 100644 --- a/source/isaaclab/isaaclab/envs/mdp/observations.py +++ b/source/isaaclab/isaaclab/envs/mdp/observations.py @@ -198,6 +198,7 @@ def joint_pos(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg(" """ # extract the used quantities (to enable type-hinting) asset: Articulation = env.scene[asset_cfg.name] + print(f"joint pos {asset.data.joint_pos[:, asset_cfg.joint_ids]}") return asset.data.joint_pos[:, asset_cfg.joint_ids] diff --git a/source/isaaclab_mimic/isaaclab_mimic/datagen/generation.py b/source/isaaclab_mimic/isaaclab_mimic/datagen/generation.py index 1390fdf48eaf..cfdb3ec5e079 100644 --- a/source/isaaclab_mimic/isaaclab_mimic/datagen/generation.py +++ b/source/isaaclab_mimic/isaaclab_mimic/datagen/generation.py @@ -123,9 +123,9 @@ def env_loop( # print("*" * 50, "\033[K") print(summary_text) log_file = "docs/generation_log.txt" - # with open(log_file, 'a') as f: - # f.write(summary_text) - # f.write("\n" + "="*80 + "\n\n") + with open(log_file, 'w') as f: + f.write(summary_text) + f.write("\n" + "="*80 + "\n\n") # termination condition is on enough successes if @guarantee_success or enough attempts otherwise diff --git a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py index 972daecbb642..efd1cee39abd 100755 --- a/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py +++ b/source/isaaclab_mimic/isaaclab_mimic/envs/cube_mimic_env_cfg.py @@ -8,7 +8,7 @@ #from isaaclab_tasks.manager_based.manipulation.cube_lift.config.franka.dev_ik_rel_env_place import FrankaDevEnvCfg #from isaaclab_tasks.manager_based.manipulation.cube_lift.config.franka.dev_ik_rel_env_obs import FrankaDevEnvCfg -from isaaclab_tasks.manager_based.manipulation.cube_lift.config.franka.dev_ik_rel_vial_insert import FrankaDevEnvCfg +from isaaclab_tasks.manager_based.manipulation.cube_lift.config.franka.dev_ik_rel_vial_insert_top_down import FrankaDevEnvCfg @configclass class CubeMimicEnvCfg(FrankaDevEnvCfg, MimicEnvCfg): diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py index d02f0da3a92a..4e043f645a0c 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/__init__.py @@ -187,6 +187,20 @@ disable_env_checker=True, ) +gym.register( + id="Dev-IK-Rel-Insert-v1", + entry_point="isaaclab.envs:ManagerBasedRLEnv", + kwargs={ + "env_cfg_entry_point": f"{__name__}.dev_ik_rel_vial_insert_top_down:FrankaDevEnvCfg", + "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_rnn_low_dim.json"), + "robomimic_bc_trans_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc_trans.json"), + "robomimic_hbc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/hbc.json"), + "robomimic_bcq_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bcq.json"), + "robomimic_diffusion_policy_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/diffusion_policy.json"), + + }, + disable_env_checker=True, +) gym.register( id="Dev-IK-Rel-Insert-vision-v0", diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json index 6bde3e9661aa..5286c063f046 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/agents/robomimic/bc_rnn_low_dim.json @@ -7,7 +7,7 @@ "terminal_output_to_txt": true, "log_tb": true, "log_wandb": true, - "wandb_proj_name": "vial_insert" + "wandb_proj_name": "vial_insert_1.0_mix" }, "save": { "enabled": true, @@ -55,7 +55,7 @@ "goal_mode": null, "cuda": true, "batch_size": 100, - "num_epochs": 1000, + "num_epochs": 1500, "seed": 101 }, "algo": { diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py index 2884f74bd029..b68bac84e865 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_place.py @@ -12,7 +12,7 @@ from isaaclab.assets import AssetBaseCfg from isaaclab.sim.spawners.from_files import UsdFileCfg from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR -from source.isaaclab_assets.isaaclab_assets.robots.universal_robots import UR10_CFG +#from source.isaaclab_assets.isaaclab_assets.robots.universal_robots import UR10_CFG from . import dev_env_cfg import math from isaaclab.managers import SceneEntityCfg diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_vial_insert.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_vial_insert.py index 35707cb590d8..2f6c9ff9edac 100644 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_vial_insert.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_vial_insert.py @@ -105,8 +105,8 @@ def __post_init__(self): body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), ) - self.terminations.success= DoneTerm(func=mdp.object_inserted_upright, params={"lower_object_cfg": SceneEntityCfg("vialrack")}) - + self.terminations.success= DoneTerm(func=mdp.object_inserted_upright, params={"lower_object_cfg": SceneEntityCfg("vialrack"), "upright_good_deg": 22.5}) + self.terminations.object_tipped = DoneTerm(func=mdp.object_knocked, params={"max_tilt": 65}) #self.terminations.success=DoneTerm(func=mdp.object_stacked_upright, params={"lower_object_cfg": SceneEntityCfg("scale")}) self.observations.subtask_terms.appr_goal=ObsTerm(func=mdp.is_object_lifted, params={"threshold":0.15} diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_vial_insert_top_down.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_vial_insert_top_down.py new file mode 100644 index 000000000000..6b1ba76ef207 --- /dev/null +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_vial_insert_top_down.py @@ -0,0 +1,127 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg +from isaaclab.utils import configclass +from isaaclab.assets import RigidObjectCfg, ArticulationCfg +from isaaclab.managers import ObservationTermCfg as ObsTerm +from isaaclab.managers import EventTermCfg as EventTerm +from isaaclab.assets import AssetBaseCfg +from isaaclab.sim.spawners.from_files import UsdFileCfg +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR, NVIDIA_NUCLEUS_DIR +#from source.isaaclab_assets.isaaclab_assets.robots.universal_robots import UR10_CFG +from isaaclab_assets.robots.universal_robots import UR10_CFG +from . import dev_env_cfg +import isaaclab.sim as sim_utils +from isaaclab.sensors import CameraCfg +import math +from isaaclab.managers import SceneEntityCfg +from isaaclab_assets.glassware.glassware import ChemistryGlassware +from isaaclab_tasks.manager_based.manipulation.cube_lift import mdp +from isaaclab_tasks.manager_based.manipulation.cube_lift.mdp import franka_stack_events + +from isaaclab.managers import TerminationTermCfg as DoneTerm +## +# Pre-defined configs +## +from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip +from isaaclab_assets.robots.universal_robots import UR10e_ROBOTIQ_GRIPPER_CFG + +## add some cameras in + + + +@configclass +class FrankaDevEnvCfg(dev_env_cfg.FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # put the beaker on the stir plate + glassware = ChemistryGlassware() + self.scene.stirplate = glassware.stirplate(pos=[0.5, 0.0, 0.01]) + #self.scene.scale = glassware.scale(pos=[0.3, -0.3, 0.01]) + self.scene.vialrack = glassware.line_vial_rack(pos=[0.3, -0.3, 0.0], scale=1.5) + self.scene.object = glassware.capped_vial(pos=[0.5, 0.0, 0.01], scale =1.0, name="object") + self.observations.policy.target_object_position = ObsTerm(func=mdp.target_position, params={"object_cfg": SceneEntityCfg("vialrack")}) + + ### subtask + self.observations.subtask_terms.stacked = ObsTerm( + func=mdp.object_stacked, + params={ + "robot_cfg": SceneEntityCfg("robot"), + "upper_object_cfg": SceneEntityCfg("object"), + "lower_object_cfg": SceneEntityCfg("vialrack"), + }, + ) + + self.events.randomise_object_scale= EventTerm( + func=mdp.randomize_rigid_body_scale, + mode="prestartup", + params={ + "scale_range": {"x": (1.0, 1.0), "y": (1.0, 1.0), "z": (1.0, 1.0)}, + "asset_cfg": SceneEntityCfg("object"), + }, + ) + self.events.reset_object_position = EventTerm( + func=mdp.reset_place_root_state_uniform, + mode="reset", + params={ + "pose_range": {"x": (0, 0.2), "y": (0, 0.25), "z": (0.02, 0.02)}, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("object"), + "asset2_cfg" : SceneEntityCfg("stirplate"), + "asset3_cfg" : SceneEntityCfg("vialrack") + }, + ) + self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + #self.scene.robot = UR10e_ROBOTIQ_GRIPPER_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + # self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace( + # prim_path="{ENV_REGEX_NS}/Robot", + # init_state=ArticulationCfg.InitialStateCfg( + # joint_pos={ + + # "panda_joint1": 0.3281, + # "panda_joint2": -0.3684, + # "panda_joint3": -0.2787, + # "panda_joint4": -2.6138, + # "panda_joint5": -2.7527, + # "panda_joint6": 2.4991, # +90° → keeps hand level + # "panda_joint7": 0.3331, + # "panda_finger_joint1": 0.04, # open gripper + # "panda_finger_joint2": 0.04, + # } + # ), + # ) + # replace with relative position controller + self.actions.arm_action = DifferentialInverseKinematicsActionCfg( + asset_name="robot", + joint_names=["panda_joint.*"], + body_name="panda_hand", + controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), + scale=0.5, + body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.0]), + ) + + self.terminations.success= DoneTerm(func=mdp.object_inserted_upright, params={"lower_object_cfg": SceneEntityCfg("vialrack"), "upright_good_deg": 22.5}) + + #self.terminations.success=DoneTerm(func=mdp.object_stacked_upright, params={"lower_object_cfg": SceneEntityCfg("scale")}) + + self.observations.subtask_terms.appr_goal=ObsTerm(func=mdp.is_object_lifted, params={"threshold":0.15} + + ) + + + +@configclass +class FrankaCubeEnvCfg_PLAY(FrankaDevEnvCfg): + def __post_init__(self): + # post init of parent + super().__post_init__() + # make a smaller scene for play + self.scene.num_envs = 50 + self.scene.env_spacing = 2.5 + # disable randomization for play + self.observations.policy.enable_corruption = False diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py index 07ff54a32231..13295c35c41b 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py @@ -352,7 +352,7 @@ def __post_init__(self): # general settings self.decimation = 2 - self.episode_length_s = 5000 + self.episode_length_s = 50 # simulation settings self.sim.dt = 0.01 # 100Hz self.sim.render_interval = self.decimation diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py index a74a3b97cd11..5754c9983564 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/observations.py @@ -34,7 +34,7 @@ def object_position_in_robot_root_frame( object_pos_b, _ = subtract_frame_transforms( robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], object_pos_w ) - + #print(f"object position : {object_pos_b}") return object_pos_b def obstacle_position_in_robot_root_frame( @@ -162,14 +162,14 @@ def is_object_lifted( def ee_frame_pos(env: ManagerBasedRLEnv, ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame")) -> torch.Tensor: ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] ee_frame_pos = ee_frame.data.target_pos_w[:, 0, :] - env.scene.env_origins[:, 0:3] - #print(f"EE pos : {ee_frame_pos}") + # print(f"EE pos : {ee_frame_pos}") return ee_frame_pos - +## def ee_frame_quat(env: ManagerBasedRLEnv, ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame")) -> torch.Tensor: ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] ee_frame_quat = ee_frame.data.target_quat_w[:, 0, :] - #print(f"EE quat : {ee_frame_quat}") + # print(f"EE quat : {ee_frame_quat}") return ee_frame_quat @@ -249,7 +249,7 @@ def robot_pose( robot_cfg: SceneEntityCfg = SceneEntityCfg("robot") ): robot: Articulation = env.scene[robot_cfg.name] - # print(robot.data.joint_pos) + print(f"robot data : {robot.data.joint_pos}") return torch.tensor([0.04]) def position_command_error( @@ -301,7 +301,7 @@ def object_tilt (env: ManagerBasedRLEnv, upper_object_cfg: SceneEntityCfg = Scen #print("obs term : tilt : ", tilt_deg) return tilt_deg -def object_knocked(env: ManagerBasedRLEnv, upper_object_cfg: SceneEntityCfg = SceneEntityCfg("object"), max_tilt: float = 45): +def object_knocked(env: ManagerBasedRLEnv, upper_object_cfg: SceneEntityCfg = SceneEntityCfg("object"), max_tilt: float = 90): object: RigidObject = env.scene[upper_object_cfg.name] tilt_deg = upright_tilt_deg(object.data.root_quat_w, object.data.default_root_state[:, 3:7]) # Return a bool tensor with shape (num_envs,) for proper batching diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/terminations.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/terminations.py index 399ab972ffeb..88d25088b996 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/terminations.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/mdp/terminations.py @@ -144,6 +144,7 @@ def object_stacked_upright(env: ManagerBasedRLEnv, robot_cfg: SceneEntityCfg = S def upright_tilt_deg(q_cur, q_init) -> torch.Tensor: u_cur, u_init = matrix_from_quat(q_cur)[:, :, 2], matrix_from_quat(q_init)[:, :, 2] + # print(f"upright good {torch.rad2deg(torch.acos((u_cur * u_init).sum(-1).clamp(-1.0, 1.0)))}") return torch.rad2deg(torch.acos((u_cur * u_init).sum(-1).clamp(-1.0, 1.0))) @@ -160,7 +161,7 @@ def object_stacked(env: ManagerBasedRLEnv, robot_cfg: SceneEntityCfg = SceneEnti pos_diff = upper_object.data.root_pos_w - lower_object.data.root_pos_w height_dist = torch.linalg.vector_norm(pos_diff[:, 2:], dim=1) xy_dist = torch.linalg.vector_norm(pos_diff[:, :2], dim=1) - #print(f"For DEBUG : xy_dist : {xy_dist}, height_dist : {height_dist}") + # print(f"For DEBUG : xy_dist : {xy_dist}, height_dist : {height_dist}") stacked = torch.logical_and(xy_dist < xy_threshold, (height_dist - height_diff) < height_threshold) # stacked = torch.logical_and(torch.isclose(robot.data.joint_pos[:, -1], # gripper_open_val.to(env.device), atol=1e-4, rtol=1e-4), stacked) @@ -171,21 +172,23 @@ def object_stacked(env: ManagerBasedRLEnv, robot_cfg: SceneEntityCfg = SceneEnti def object_inserted_upright(env: ManagerBasedRLEnv, robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), upper_object_cfg: SceneEntityCfg = SceneEntityCfg("object"),lower_object_cfg: SceneEntityCfg = SceneEntityCfg("vialrack"), - xy_threshold: float = 0.01, height_threshold: float = 0.03, height_diff: float = 0.0, + xy_threshold: float = 0.01, height_threshold: float = 0.01, height_diff: float = 0.03, atol=0.0001, rtol=0.0001, - upright_good_deg: float = 30.0, gripper_open_val: torch.Tensor = torch.tensor([0.04]), logging=False) -> torch.Tensor: + upright_good_deg: float = 35, gripper_open_val: torch.Tensor = torch.tensor([0.04]), logging=False) -> torch.Tensor: """Stacked AND sufficiently upright (≤ upright_good_deg).""" robot: Articulation = env.scene[robot_cfg.name] upper: RigidObject = env.scene[upper_object_cfg.name] tilt_deg = upright_tilt_deg(upper.data.root_quat_w, upper.data.default_root_state[:, 3:7]) + #print(f"Tilt deg: {tilt_deg}") upright_good = tilt_deg <= upright_good_deg stacked = object_stacked(env, robot_cfg, upper_object_cfg, lower_object_cfg, xy_threshold, height_threshold, height_diff, gripper_open_val) #print(f"For DEBUG : STACKED STATUS : {stacked}") stacked_upright = stacked & upright_good + #print(f"For DEBUG : STACKED STATUS : {stacked_upright}, {stacked}, {upright_good} ") gripper_joint_ids, _ = robot.find_joints(env.cfg.gripper_joint_names) stacked = torch.logical_and( torch.isclose( From ceaa45bc034966f8865d8c8eff241e124b849dbe Mon Sep 17 00:00:00 2001 From: LJones-alt Date: Fri, 13 Mar 2026 17:52:19 +0000 Subject: [PATCH 14/15] ready for case! --- .gitignore | 60 ++ .vscode/browse.vc.db-shm | Bin 32768 -> 0 bytes .vscode/browse.vc.db-wal | 0 debug_ci.py | 26 + docker/.env.ros2 | 10 +- docker/.ros/cyclonedds.xml | 15 - docker/.ros/fastdds.xml | 46 +- docker/Dockerfile.ros2 | 19 +- docs/find_best_checkpoints.py | 2 +- docs/run_experiments.sh | 64 +- ros2/src | 2 +- .../environments/teleoperation/ros2_bridge.py | 130 +++ .../teleoperation/teleop_se3_agent.py | 168 +++- .../robomimic/backup_controller.py | 2 +- .../robomimic/backup_controller_insert_top.py | 16 +- .../robomimic/play_ensemble_ros2.py | 921 +++++++++++++++++ .../robomimic/play_ensemble_ros2_backup.py | 924 ++++++++++++++++++ .../robomimic/play_ensemble_v05.py | 163 ++- .../config/franka/dev_ik_rel_env_obs.py | 2 + .../franka/dev_ik_rel_vial_insert_top_down.py | 2 +- .../manipulation/cube_lift/lift_env_cfg.py | 6 +- 21 files changed, 2393 insertions(+), 185 deletions(-) delete mode 100644 .vscode/browse.vc.db-shm delete mode 100644 .vscode/browse.vc.db-wal create mode 100644 debug_ci.py delete mode 100644 docker/.ros/cyclonedds.xml create mode 100644 scripts/environments/teleoperation/ros2_bridge.py create mode 100644 scripts/imitation_learning/robomimic/play_ensemble_ros2.py create mode 100644 scripts/imitation_learning/robomimic/play_ensemble_ros2_backup.py diff --git a/.gitignore b/.gitignore index 4cf7259980ca..149e1747f357 100644 --- a/.gitignore +++ b/.gitignore @@ -39,6 +39,7 @@ docker/.container.cfg # Don't ignore the top-level .vscode directory as it is # used to configure VS Code settings !.vscode +**/.vscode/*.db* # Outputs **/output/* @@ -77,3 +78,62 @@ tests/ # Docker history .isaac-lab-docker-history +docs/insert/2k/Dev-IK-Rel-Insert-v0/model0/vial_insert/20260218174413/config.json +docs/insert/2k/Dev-IK-Rel-Insert-v0/model1/vial_insert/20260218174413/config.json +docs/insert/2k/Dev-IK-Rel-Insert-v0/model10/vial_insert/20260218174413/config.json +docs/insert/2k/Dev-IK-Rel-Insert-v0/model11/vial_insert/20260218174413/config.json +docs/insert/2k/Dev-IK-Rel-Insert-v0/model12/vial_insert/20260218174413/config.json +docs/insert/2k/Dev-IK-Rel-Insert-v0/model13/vial_insert/20260218174413/config.json +docs/insert/2k/Dev-IK-Rel-Insert-v0/model14/vial_insert/20260218174413/config.json +docs/insert/2k/Dev-IK-Rel-Insert-v0/model2/vial_insert/20260218174413/config.json +docs/insert/2k/Dev-IK-Rel-Insert-v0/model3/vial_insert/20260218174413/config.json +docs/insert/2k/Dev-IK-Rel-Insert-v0/model4/vial_insert/20260218174413/config.json +docs/insert/2k/Dev-IK-Rel-Insert-v0/model5/vial_insert/20260218174413/config.json +docs/insert/2k/Dev-IK-Rel-Insert-v0/model6/vial_insert/20260218174413/config.json +docs/insert/2k/Dev-IK-Rel-Insert-v0/model7/vial_insert/20260218174413/config.json +docs/insert/2k/Dev-IK-Rel-Insert-v0/model8/vial_insert/20260218174413/config.json +docs/insert/2k/Dev-IK-Rel-Insert-v0/model9/vial_insert/20260218174413/config.json +docs/insert/4k/model0/config.json +docs/insert/4k/model1/config.json +docs/insert/4k/model10/config.json +docs/insert/4k/model11/config.json +docs/insert/4k/model12/config.json +docs/insert/4k/model13/config.json +docs/insert/4k/model14/config.json +docs/insert/4k/model2/config.json +docs/insert/4k/model3/config.json +docs/insert/4k/model4/config.json +docs/insert/4k/model5/config.json +docs/insert/4k/model6/config.json +docs/insert/4k/model7/config.json +docs/insert/4k/model8/config.json +docs/insert/4k/model9/config.json +docs/insert/Dev-IK-Rel-Insert-v0/model0/vial_insert/20260217091333/config.json +docs/insert/Dev-IK-Rel-Insert-v0/model1/vial_insert/20260217091333/config.json +docs/insert/Dev-IK-Rel-Insert-v0/model10/vial_insert/20260217091333/config.json +docs/insert/Dev-IK-Rel-Insert-v0/model11/vial_insert/20260217091333/config.json +docs/insert/Dev-IK-Rel-Insert-v0/model12/vial_insert/20260217091333/config.json +docs/insert/Dev-IK-Rel-Insert-v0/model13/vial_insert/20260217091333/config.json +docs/insert/Dev-IK-Rel-Insert-v0/model14/vial_insert/20260217091333/config.json +docs/insert/Dev-IK-Rel-Insert-v0/model2/vial_insert/20260217091333/config.json +docs/insert/Dev-IK-Rel-Insert-v0/model3/vial_insert/20260217091333/config.json +docs/insert/Dev-IK-Rel-Insert-v0/model4/vial_insert/20260217091333/config.json +docs/insert/Dev-IK-Rel-Insert-v0/model5/vial_insert/20260217091333/config.json +docs/insert/Dev-IK-Rel-Insert-v0/model6/vial_insert/20260217091333/config.json +docs/insert/Dev-IK-Rel-Insert-v0/model7/vial_insert/20260217091333/config.json +docs/insert/Dev-IK-Rel-Insert-v0/model8/vial_insert/20260217091333/config.json +docs/insert/Dev-IK-Rel-Insert-v0/model9/vial_insert/20260217091333/config.json +docs/insert/Dev-IK-Rel-Insert-v0_old/model0/vial_insert/20260216134802/config.json +docs/insert/Dev-IK-Rel-Insert-v0_old/model1/vial_insert/20260216134805/config.json +docs/insert/Dev-IK-Rel-Insert-v0_old/model10/vial_insert/20260216134809/config.json +docs/insert/Dev-IK-Rel-Insert-v0_old/model11/vial_insert/20260216134809/config.json +docs/insert/Dev-IK-Rel-Insert-v0_old/model12/vial_insert/20260216134810/config.json +docs/insert/Dev-IK-Rel-Insert-v0_old/model13/vial_insert/20260216134810/config.json +docs/insert/Dev-IK-Rel-Insert-v0_old/model14/vial_insert/20260216134810/config.json +docs/insert/Dev-IK-Rel-Insert-v0_old/model2/vial_insert/20260216134806/config.json +docs/insert/Dev-IK-Rel-Insert-v0_old/model3/vial_insert/20260216134806/config.json +docs/insert/Dev-IK-Rel-Insert-v0_old/model4/vial_insert/20260216134806/config.json +docs/insert/Dev-IK-Rel-Insert-v0_old/model6/vial_insert/20260216134807/config.json +docs/insert/Dev-IK-Rel-Insert-v0_old/model7/vial_insert/20260216134808/config.json +docs/insert/Dev-IK-Rel-Insert-v0_old/model8/vial_insert/20260216134808/config.json +docs/insert/Dev-IK-Rel-Insert-v0_old/model9/vial_insert/20260216134808/config.json diff --git a/.vscode/browse.vc.db-shm b/.vscode/browse.vc.db-shm deleted file mode 100644 index 25a1415fef5f27b23b8308c46d49d3b9353ec0cd..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 32768 zcmeI*xlIF65C-698_a3$!=Vuo0YYv88sJKi=ztC=fD95yD1np$98(2!K>*AKD{X48 zWxYk;lSh7g%+t5|<^K8U;p~0%);sC; z$GpFoSFYd6D$kYw?bvjpR-y(`2T_+%S5c8GEk{+aoyYM-RC!PJT5-PUD`&>mB0zuu z0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+009C72oNAZ zfB*pk1PBlyK!5-N0t5&U$WmZ2%Tz)Un1w(q?aksbo - - - - - - - true - - - auto - 120 - - - diff --git a/docker/.ros/fastdds.xml b/docker/.ros/fastdds.xml index 93ca9c403dad..7cb87abfc031 100644 --- a/docker/.ros/fastdds.xml +++ b/docker/.ros/fastdds.xml @@ -1,27 +1,49 @@ + + - UdpTransport + TailscaleUDP UDPv4 + + +
    100.107.19.48
    +     - + - UdpTransport + TailscaleUDP false + + + + + +
    100.71.76.112
    +     +     +     + + + +
    100.71.76.112
    +     +     +     + true +     diff --git a/docker/Dockerfile.ros2 b/docker/Dockerfile.ros2 index dfc555ce31c2..4291c15ffae0 100644 --- a/docker/Dockerfile.ros2 +++ b/docker/Dockerfile.ros2 @@ -25,7 +25,24 @@ RUN --mount=type=cache,target=/var/cache/apt \ ros-humble-rmw-cyclonedds-cpp \ ros-humble-rmw-fastrtps-cpp \ # This includes various dev tools including colcon - ros-dev-tools && \ + ros-dev-tools \ + # Networking tools + iproute2 \ + net-tools \ + # libfranka build dependencies (required for v0.15+ / v0.19) + libpoco-dev \ + libeigen3-dev \ + libssl-dev \ + libspdlog-dev \ + # franka_ros2 / franka_gripper dependencies (minimal, no MoveIt) + ros-humble-control-msgs \ + ros-humble-controller-manager \ + ros-humble-hardware-interface \ + ros-humble-realtime-tools \ + ros-humble-ros2-control \ + ros-humble-ros2-controllers \ + ros-humble-rclcpp-action \ + python3-vcstool && \ # Install rosdeps for extensions that declare a ros_ws in # their extension.toml ${ISAACLAB_PATH}/isaaclab.sh -p ${ISAACLAB_PATH}/tools/install_deps.py rosdep ${ISAACLAB_PATH}/source && \ diff --git a/docs/find_best_checkpoints.py b/docs/find_best_checkpoints.py index 4c35e1afe447..bbb7fe12b9cd 100644 --- a/docs/find_best_checkpoints.py +++ b/docs/find_best_checkpoints.py @@ -72,7 +72,7 @@ def main(): """Main function to process all model directories.""" # Define paths - handle both host and Docker container paths script_dir = Path(__file__).parent.resolve() - base_dir = script_dir / "/workspace/isaaclab/robomimic/Dev-IK-Rel-Insert-v0" + base_dir = script_dir / "/workspace/isaaclab/robomimic/Dev-IK-Rel-Insert-v1" output_dir = base_dir / "best_models" # Create output directory (including parent directories if needed) diff --git a/docs/run_experiments.sh b/docs/run_experiments.sh index 586f3e9950fe..c88d7116dc09 100644 --- a/docs/run_experiments.sh +++ b/docs/run_experiments.sh @@ -2,11 +2,11 @@ ## script to run experiments in the background -task="Dev-IK-Rel-Insert-v0" -horizon=2000 +task="Dev-IK-Rel-Insert-v1" +horizon=1000 num_rollouts=100 run_file="scripts/imitation_learning/robomimic/play_ensemble_v05.py" -exp_type="insert_recal_2k_0.9" +exp_type="insert_top_none" ##### this now runs the experiments @@ -24,30 +24,44 @@ seed=115 exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" ./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless -# ## ensemble 10 -ensemble_size=10 -seed=101 -exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" -./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless +# # ## ensemble 10 +# ensemble_size=10 +# seed=101 +# exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" +# ./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless -seed=107 -exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" -./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless +# seed=107 +# exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" +# ./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless -seed=115 -exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" -./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless +# seed=115 +# exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" +# ./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless -## ensemble 5 -ensemble_size=5 -seed=101 -exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" -./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless +# ## ensemble 5 +# ensemble_size=5 +# seed=101 +# exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" +# ./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless -seed=107 -exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" -./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless +# seed=107 +# exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" +# ./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless -seed=115 -exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" -./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless +# seed=115 +# exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" +# ./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless + + +# ensemble_size=1 +# seed=101 +# exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" +# ./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless + +# seed=107 +# exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" +# ./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless + +# seed=115 +# exp_name="${task}_ensemble_${ensemble_size}_seed_${seed}_${exp_type}" +# ./isaaclab.sh -p $run_file --task $task --horizon $horizon --num_rollouts $num_rollouts --ensemble_size $ensemble_size --seed $seed --exp_name $exp_name --headless diff --git a/ros2/src b/ros2/src index a739caa35161..7ef0ab00964a 160000 --- a/ros2/src +++ b/ros2/src @@ -1 +1 @@ -Subproject commit a739caa35161035f3a4505fb077068e9adf51172 +Subproject commit 7ef0ab00964a0d2b5e19e9292bbcef9f32dda811 diff --git a/scripts/environments/teleoperation/ros2_bridge.py b/scripts/environments/teleoperation/ros2_bridge.py new file mode 100644 index 000000000000..8705e2b2808e --- /dev/null +++ b/scripts/environments/teleoperation/ros2_bridge.py @@ -0,0 +1,130 @@ +#!/usr/bin/env python3 +import rclpy +from rclpy.node import Node +from rclpy.action import ActionClient +from geometry_msgs.msg import PoseStamped +from sensor_msgs.msg import JointState +from franka_msgs.action import Move +import socket +import json +import threading +import time + +class ROS2Bridge(Node): + def __init__(self, port=5005): + super().__init__('isaaclab_ros2_bridge') + self.get_logger().info(f'Starting ROS2 Bridge on port {port}...') + + # ROS2 Communication + self._target_pub = self.create_publisher(PoseStamped, '/cartesian_target', 10) + self._joint_target_pub = self.create_publisher(JointState, '/action_desired_joint', 10) + self._action_client = ActionClient(self, Move, 'fr3_gripper/franka_gripper/move') + self._real_pose_sub = self.create_subscription(PoseStamped, '/franka_robot_state_broadcaster/current_pose', self._real_pose_callback, 10) + self._joint_sub = self.create_subscription(JointState, '/joint_states', self._joint_callback, 10) + + self.latest_real_pose = None + self.latest_joints = None + self.lock = threading.Lock() + + # UDP Communication + self.sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) + self.sock.bind(('127.0.0.1', port)) + self.sock.settimeout(0.1) + + self.get_logger().info('Bridge is ready. Waiting for simulation data...') + + def _real_pose_callback(self, msg): + with self.lock: + self.latest_real_pose = { + 'pos': [msg.pose.position.x, msg.pose.position.y, msg.pose.position.z], + 'quat': [msg.pose.orientation.x, msg.pose.orientation.y, msg.pose.orientation.z, msg.pose.orientation.w] + } + + def _joint_callback(self, msg): + arm_joints = [f'fr3_joint{i}' for i in range(1, 8)] + pos, vel = [], [] + try: + for name in arm_joints: + idx = msg.name.index(name) + pos.append(msg.position[idx]) + vel.append(msg.velocity[idx]) + with self.lock: + self.latest_joints = {'pos': pos, 'vel': vel} + except (ValueError, IndexError): + pass + + def publish_cartesian(self, data): + if len(data) < 7: return + msg = PoseStamped() + msg.header.stamp = self.get_clock().now().to_msg() + msg.header.frame_id = 'fr3_link0' + msg.pose.position.x, msg.pose.position.y, msg.pose.position.z = data[0:3] + msg.pose.orientation.x, msg.pose.orientation.y, msg.pose.orientation.z, msg.pose.orientation.w = data[3:7] + self._target_pub.publish(msg) + self.get_logger().info(f"Published Cartesian Target: {data[0:3]}", throttle_duration_sec=0.5) + + def publish_joints(self, positions): + if len(positions) < 7: return + msg = JointState() + msg.header.stamp = self.get_clock().now().to_msg() + msg.header.frame_id = 'base' + msg.name = [f'fr3_joint{i}' for i in range(1, 8)] + msg.position = [float(p) for p in positions[:7]] + msg.velocity = [0.0] * 7 + msg.effort = [0.0] * 7 + self._joint_target_pub.publish(msg) + # Throttled debug log (every 1s) + self.get_logger().info(f"Published Joint Target: {[round(p, 3) for p in msg.position]}", throttle_duration_sec=0.2) + + def send_gripper(self, width): + self.get_logger().info(f"Sending Gripper Goal: width={width}") + goal_msg = Move.Goal() + goal_msg.width = width + goal_msg.speed = 0.2 + self._action_client.wait_for_server(timeout_sec=1.0) + self._action_client.send_goal_async(goal_msg) + + def run(self): + while rclpy.ok(): + try: + data, addr = self.sock.recvfrom(1024) + msg = json.loads(data.decode()) + + if msg['type'] == 'cartesian': + # print(f"DEBUG: Received cartesian from {addr}") + self.publish_cartesian(msg['data']) + elif msg['type'] == 'joint': + # print(f"DEBUG: Received joint from {addr}") + self.publish_joints(msg['data']) + elif msg['type'] == 'gripper': + print(f"DEBUG: Received gripper command: {msg['width']}") + self.send_gripper(msg['width']) + elif msg['type'] == 'query': + # print(f"DEBUG: Received query from {addr}") + # Send back latest state + with self.lock: + resp = { + 'real_pose': self.latest_real_pose, + 'joints': self.latest_joints + } + self.sock.sendto(json.dumps(resp).encode(), addr) + except socket.timeout: + pass + except Exception as e: + self.get_logger().error(f'Bridge error: {e}') + + rclpy.spin_once(self, timeout_sec=0.01) + +def main(): + rclpy.init() + bridge = ROS2Bridge() + try: + bridge.run() + except KeyboardInterrupt: + pass + finally: + bridge.destroy_node() + rclpy.shutdown() + +if __name__ == '__main__': + main() diff --git a/scripts/environments/teleoperation/teleop_se3_agent.py b/scripts/environments/teleoperation/teleop_se3_agent.py index 77399998e8d8..39c39db27b62 100644 --- a/scripts/environments/teleoperation/teleop_se3_agent.py +++ b/scripts/environments/teleoperation/teleop_se3_agent.py @@ -65,14 +65,91 @@ from isaaclab_tasks.manager_based.manipulation.lift import mdp from isaaclab_tasks.utils import parse_env_cfg -# # import rclpy -# # from rclpy.node import Node -# from std_msgs.msg import Float32MultiArray -# from sensor_msgs.msg import JointState - - -if args_cli.enable_pinocchio: - import isaaclab_tasks.manager_based.manipulation.pick_place # noqa: F401 +# --- UDP Bridge imports --- +import socket +import json +import time +import numpy as np + +class BridgeClient: + def __init__(self, port=5005): + self.sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) + self.addr = ('127.0.0.1', port) + self.sock.settimeout(0.5) + self._last_print_time = 0 + + def publish_command(self, actions): + """Publish 7-DOF Cartesian target: [x, y, z, qx, qy, qz, qw]""" + if hasattr(actions, 'cpu'): + act_list = actions.cpu().numpy().flatten().tolist() + else: + act_list = np.array(actions).flatten().tolist() + + if len(act_list) < 7: return + + payload = {'type': 'cartesian', 'data': act_list[:7]} + self.sock.sendto(json.dumps(payload).encode(), self.addr) + + # Throttled debug print (every 1 second) + curr_time = time.time() + if curr_time - self._last_print_time > 1.0: + print(f"DEBUG: Sent Cartesian to Bridge: {act_list}") + self._last_print_time = curr_time + + def publish_joints(self, positions): + """Publish 7-DOF Joint positions""" + if hasattr(positions, 'cpu'): + pos_list = positions.cpu().numpy().flatten().tolist() + else: + pos_list = np.array(positions).flatten().tolist() + + if len(pos_list) < 7: return + + payload = {'type': 'joint', 'data': pos_list[:7]} + self.sock.sendto(json.dumps(payload).encode(), self.addr) + + # Throttled debug print (every 1s) + curr_time = time.time() + if curr_time - self._last_print_time > 1.0: + print(f"DEBUG: Sent Joint Target to Bridge: {[round(p, 3) for p in pos_list[:7]]}") + self._last_print_time = curr_time + + def open_gripper(self): + print("DEBUG: Sending Open Gripper to Bridge") + payload = {'type': 'gripper', 'width': 0.08} + self.sock.sendto(json.dumps(payload).encode(), self.addr) + + def close_gripper(self): + print("DEBUG: Sending Close Gripper to Bridge") + payload = {'type': 'gripper', 'width': 0.0} + self.sock.sendto(json.dumps(payload).encode(), self.addr) + + def get_robot_state(self): + payload = {'type': 'query'} + self.sock.sendto(json.dumps(payload).encode(), self.addr) + try: + data, _ = self.sock.recvfrom(2048) + return json.loads(data.decode()) + except socket.timeout: + return None + + def init_real(self, sim_pose): + print("[INFO] Initializing real robot synchronization via bridge...") + self.publish_command(sim_pose) + target_pos = np.array(sim_pose[:3]) + + while True: + state = self.get_robot_state() + if state and state.get('real_pose'): + real_pos = np.array(state['real_pose']['pos']) + dist = np.linalg.norm(target_pos - real_pos) + print(f"[INFO] Distance to target: {dist:.4f}m") + if dist < 0.05: + print("[INFO] Real robot synchronized with simulation!") + break + else: + print("[INFO] Waiting for bridge on 127.0.0.1:5005...") + time.sleep(0.5) def main() -> None: @@ -85,6 +162,9 @@ def main() -> None: Returns: None """ + # Initialize UDP Bridge Client + bridge = BridgeClient() + # parse configuration env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=args_cli.num_envs) env_cfg.env_name = args_cli.task @@ -229,6 +309,31 @@ def stop_teleoperation() -> None: # reset environment env.reset() teleop_interface.reset() + prev_gripper_action = None + + # Sync real robot with simulation initial pose + if bridge is not None: + # Get simulated robot initial EE pose + try: + # We use the observation manager to get the tracked end-effector pose + # Based on your environment info, these keys are available in the 'policy' group + obs_dict = env.observation_manager.compute_group("policy") + + # eef_pos is (num_envs, 3), eef_quat is (num_envs, 4) + eef_pos = obs_dict["eef_pos"][0].cpu().numpy() + eef_quat = obs_dict["eef_quat"][0].cpu().numpy() # [qw, qx, qy, qz] + + # ROS expects [qx, qy, qz, qw], Isaac uses [qw, qx, qy, qz] + sim_init_pose = [ + float(eef_pos[0]), float(eef_pos[1]), float(eef_pos[2]), + float(eef_quat[1]), float(eef_quat[2]), float(eef_quat[3]), float(eef_quat[0]) + ] + sim_pose = [0.5208, 0.0096, 0.3751, 0.0360, 0.7414, -0.0706, 0.6663] + print(f"Simulated robot initial pose: {sim_init_pose}") + # bridge.init_real(sim_init_pose) + except Exception as e: + print(f"[WARN] Could not retrieve sim initial pose from observations: {e}") + print("[INFO] Skipping real robot synchronization.") print("Teleoperation started. Press 'R' to reset the environment.") @@ -243,9 +348,48 @@ def stop_teleoperation() -> None: # Only apply teleop commands when active if teleoperation_active: # process actions - actions = action.repeat(env.num_envs, 1) - # process_action(actions) - # apply actions + actions = action.repeat(env.num_envs, 1).to(env.device) + + # Compute absolute pose for the ROS bridge + with torch.no_grad(): + # Get latest observations from the 'policy' group + obs_dict = env.observation_manager.compute_group("policy") + eef_pos = obs_dict["eef_pos"] # (num_envs, 3) + eef_quat = obs_dict["eef_quat"] # (num_envs, 4) -> [qw, qx, qy, qz] + + # Calculate absolute target for bridge (current + delta) + # actions[:, 0:3] is the position delta + abs_pos = eef_pos + actions[:, 0:3] + + # For orientation, we map Isaac [qw, qx, qy, qz] to ROS [qx, qy, qz, qw] + # and keep the current orientation (or integrate if needed, but simple is better) + abs_quat_ros = torch.cat([eef_quat[:, 1:], eef_quat[:, 0:1]], dim=-1) + + # Construct a 7-DOF absolute pose [x, y, z, qx, qy, qz, qw] + abs_pose_bridge = torch.cat([abs_pos, abs_quat_ros], dim=-1) + print(f"absolute pose: {eef_pos}, {eef_quat}") + print(f"absolute pose action: {abs_pose_bridge}") + # ROS 2 Synchronization via Bridge + if bridge is not None: + # Send absolute pose to bridge + bridge.publish_command(abs_pose_bridge[0]) + + # Send joint positions for MoveIt 2 + obs_dict_full = env.observation_manager.compute_group("policy") + if "joint_pos" in obs_dict_full: + bridge.publish_joints(obs_dict_full["joint_pos"][0]) + + # Handle gripper toggling + curr_gripper = float(actions[0, -1]) + if prev_gripper_action is not None: + if curr_gripper != prev_gripper_action: + if curr_gripper > 0: + bridge.open_gripper() + else: + bridge.close_gripper() + prev_gripper_action = curr_gripper + + # apply actions (relative as intended by the environment) env.step(actions) else: env.sim.render() @@ -259,6 +403,8 @@ def stop_teleoperation() -> None: break # close the simulator + env.close() + env.close() print("Environment closed") diff --git a/scripts/imitation_learning/robomimic/backup_controller.py b/scripts/imitation_learning/robomimic/backup_controller.py index f529e417e227..f6a3fbd778dd 100644 --- a/scripts/imitation_learning/robomimic/backup_controller.py +++ b/scripts/imitation_learning/robomimic/backup_controller.py @@ -75,7 +75,7 @@ def infer_state_machine( rest_rot = wp.transform_get_rotation(rest_ee_pose[tid]) # If we're below safe height, first move up - safe_height = 0.35 # Safe z-height to avoid collisions + safe_height = 0.15 # Safe z-height to avoid collisions if current_ee_pos.z < safe_height: # Lift straight up first lift_pos = wp.vec3(current_ee_pos.x, current_ee_pos.y, safe_height) diff --git a/scripts/imitation_learning/robomimic/backup_controller_insert_top.py b/scripts/imitation_learning/robomimic/backup_controller_insert_top.py index 5a893b089255..3dcd3c68eda1 100644 --- a/scripts/imitation_learning/robomimic/backup_controller_insert_top.py +++ b/scripts/imitation_learning/robomimic/backup_controller_insert_top.py @@ -264,7 +264,7 @@ def infer_state_machine( if distance_below_threshold( wp.transform_get_translation(ee_pose[tid]), wp.transform_get_translation(des_ee_pose[tid]), - 0.01, + 0.014, ): if sm_wait_time[tid] >= BackupSMWaitTime.APPROACH_GOAL: # move to next state and reset wait time @@ -462,13 +462,13 @@ def compute(self, ee_pose: torch.Tensor, start_object_pose: torch.Tensor, curren goal_quat = goal_quat_debug[i][3:] # compute Euclidean distance with numpy dist = np.linalg.norm(cur_pos - des_pos) - print(f"StateMachine debug state {debug_state}") - # print(f"[Env {i}]") - print(f" Goal pose : {final_object_pose}") - print(f" Current -> pos: {cur_pos}, quat: {cur_quat}") - print(f" Desired -> pos: {des_pos}, quat: {des_quat}") - print(f" Goal quat -> : {goal_quat}") - print(f" Euclidean distance: {dist:.4f}") + # print(f"StateMachine debug state {debug_state}") + # # print(f"[Env {i}]") + # print(f" Goal pose : {final_object_pose}") + # print(f" Current -> pos: {cur_pos}, quat: {cur_quat}") + # print(f" Desired -> pos: {des_pos}, quat: {des_quat}") + # print(f" Goal quat -> : {goal_quat}") + # print(f" Euclidean distance: {dist:.4f}") # #print(f"returned state : {self.sm_state}") # convert transformations back to (w, x, y, z) des_ee_pose = self.des_ee_pose[:, [0, 1, 2, 6, 3, 4, 5]] diff --git a/scripts/imitation_learning/robomimic/play_ensemble_ros2.py b/scripts/imitation_learning/robomimic/play_ensemble_ros2.py new file mode 100644 index 000000000000..de3145a96921 --- /dev/null +++ b/scripts/imitation_learning/robomimic/play_ensemble_ros2.py @@ -0,0 +1,921 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# run this script: +# ./isaaclab.sh -p scripts/imitation_learning/robomimic/play_ensemble_v05.py --device cuda --task Dev-IK-Rel-v0 --num_rollouts 100 +# stack cube task: ./isaaclab.sh -p scripts/imitation_learning/robomimic/play.py --device cuda --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --num_rollouts 50 +"""Script to play and evaluate a trained policy from robomimic. + +This script loads a robomimic policy and plays it in an Isaac Lab environment. +Updated for robomimic v0.5 compatibility. + +Args: + task: Name of the environment. + checkpoint: Path to the robomimic policy checkpoint. + horizon: If provided, override the step horizon of each rollout. + num_rollouts: If provided, override the number of rollouts. + seed: If provided, overeride the default random seed. + norm_factor_min: If provided, minimum value of the action space normalization factor. + norm_factor_max: If provided, maximum value of the action space normalization factor. +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Evaluate robomimic policy for Isaac Lab environment.") +parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.") +parser.add_argument("--video_length", type=int, default=800, help="Length of the recorded video (in steps).") +parser.add_argument( + "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations." +) +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument("--checkpoint", type=str, default=None, help="Pytorch model checkpoint to load.") +parser.add_argument("--horizon", type=int, default=2000, help="Step horizon of each rollout.") +parser.add_argument("--num_rollouts", type=int, default=1, help="Number of rollouts.") +parser.add_argument("--seed", type=int, default=101, help="Random seed.") +parser.add_argument( + "--norm_factor_min", type=float, default=None, help="Optional: minimum value of the normalization factor." +) +parser.add_argument( + "--norm_factor_max", type=float, default=None, help="Optional: maximum value of the normalization factor." +) +parser.add_argument("--enable_pinocchio", default=False, action="store_true", help="Enable Pinocchio.") + +parser.add_argument("--model_name", type=str, default=None) +parser.add_argument("--exp_name", type=str, default=None) + +group = parser.add_mutually_exclusive_group() +group.add_argument("--use_recovery", type=bool ,default= False, help="Enable recovery mechanism. By default recovery is enabled.") + +parser.add_argument("--ensemble_size", type=int, default=10) + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +if args_cli.enable_pinocchio: + # Import pinocchio before AppLauncher to force the use of the version installed by IsaacLab and not the one installed by Isaac Sim + # pinocchio is required by the Pink IK controllers and the GR1T2 retargeter + import pinocchio # noqa: F401 +import numpy as np +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import time +import copy +import random +import os +import gymnasium as gym +import torch +import math +from collections import deque + +import robomimic.utils.file_utils as FileUtils +import robomimic.utils.torch_utils as TorchUtils +from isaaclab_tasks.manager_based.manipulation.cube_lift.mdp.observations import get_joint_pos, object_grasped +#from chills.tasks.mdp.observations import get_joint_pos, object_grasped +if args_cli.enable_pinocchio: + import isaaclab_tasks.manager_based.manipulation.pick_place # noqa: F401 + +from isaaclab_tasks.utils import parse_env_cfg +#from isaaclab.utils.logging_helper import LoggingHelper, ErrorType, LogType +from isaaclab.utils.math import matrix_from_quat, convert_quat, quat_mul, euler_xyz_from_quat +from evaluation import ensemble_uncertainty +from isaaclab.safety.switchingLogic import SwitchingLogic +from isaaclab.controllers import DifferentialIKController, DifferentialIKControllerCfg +from isaaclab.utils.math import subtract_frame_transforms, euler_xyz_from_quat +from isaaclab.managers import SceneEntityCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg, BinaryJointPositionActionCfg +from isaaclab.envs.mdp.actions.binary_joint_actions import BinaryJointPositionAction +from backup_controller_handler import BackupController + +# --- ROS2 imports --- +# --- UDP Bridge imports --- +import socket +import json +import time +import numpy as np + +def policy_from_checkpoint_v05(ckpt_path, device, verbose=True): + """ + Load a policy from a checkpoint file - compatible with robomimic v0.5. + + This is a wrapper that handles API differences between robomimic versions. + + Args: + ckpt_path: Path to the checkpoint file. + device: Device to load the policy on. + verbose: Whether to print verbose output. + + Returns: + policy: The loaded policy. + ckpt_dict: The checkpoint dictionary. + """ + import robomimic + from packaging import version + + # Check robomimic version + robomimic_version = getattr(robomimic, '__version__', '0.5.0') + + if verbose: + print(f"[INFO] Loading checkpoint with robomimic version {robomimic_version}") + + # robomimic v0.5+ may have different API + if version.parse(robomimic_version) >= version.parse('0.5.0'): + # v0.5 API - policy_from_checkpoint returns (policy, ckpt_dict) + try: + result = FileUtils.policy_from_checkpoint(ckpt_path=ckpt_path, device=device, verbose=verbose) + if isinstance(result, tuple): + return result + else: + return result, None + except TypeError as e: + if verbose: + print(f"[WARN] policy_from_checkpoint failed with: {e}") + print("[INFO] Trying alternative loading method...") + + # Alternative: manual loading + import torch + import json + from robomimic.algo import algo_factory + from robomimic.config import config_factory + + ckpt_dict = torch.load(ckpt_path, map_location=device) + + # Get config from checkpoint + config = ckpt_dict.get('config', {}) + if isinstance(config, dict): + algo_name = config.get('algo_name', 'bc') + config_obj = config_factory(algo_name) + with config_obj.values_unlocked(): + config_obj.update(config) + config = config_obj + + # Create policy + policy = algo_factory( + algo_name=config.algo_name, + config=config, + obs_key_shapes=ckpt_dict.get('shape_metadata', {}).get('all_shapes', {}), + ac_dim=ckpt_dict.get('shape_metadata', {}).get('ac_dim', 7), + device=device, + ) + + # Load weights + policy.deserialize(ckpt_dict['model']) + policy.set_eval() + + return policy, ckpt_dict + else: + # Older API + return FileUtils.policy_from_checkpoint(ckpt_path=ckpt_path, device=device, verbose=verbose) + + + +# tip over detection +def has_fallen(asset, threshold: float = -0.7, debug: bool = True) -> bool: + """ + Detect if an IsaacLab asset has fallen over by checking its up vector. + + Args: + asset: IsaacLab asset (e.g. robot, object) with .get_world_pose() + threshold: minimum z-component of the up vector (1.0 = perfectly upright). + Typical cutoff ~0.7 (≈45° tilt). + debug: if True, prints internal values for inspection. + + Returns: + True if fallen, False otherwise. + """ + quat = asset.data.body_link_quat_w # (pos, quat), usually torch.Tensors + # print("Beaker quat : ", quat) + # Ensure torch tensor, shape (4,) + if isinstance(quat, (list, tuple)): + quat = torch.tensor(quat, dtype=torch.float32) + quat = quat.view(-1, 4) # shape (1,4) + + # Convert quaternion to wxyz ordering + quat_wxyz = convert_quat(quat, to="wxyz") + + # Rotation matrix (1,3,3) + R = matrix_from_quat(quat_wxyz) + + # World up vector is the third column of rotation matrix + up = R[0, :, 2] # shape (3,) + uprightness = float(up[2]) + + if debug: + print(f"[has_fallen] quat={quat_wxyz.cpu().numpy().round(4)}") + print(f"[has_fallen] up={up.cpu().numpy().round(4)} uprightness={uprightness:.3f}") + + return uprightness > threshold + +def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, use_recovery=True, rollout_num=0): + """Perform a single rollout of the policy in the environment. + + Args: + policy: The robomimicpolicy to play. + env: The environment to play in. + horizon: The step horizon of each rollout. + device: The device to run the policy on. + + Returns: + terminated: Whether the rollout terminated. + traj: The trajectory of the rollout. + """ + ## do setup + print("running rollout number : ", rollout_num) + # Initialize UDP Bridge Client + bridge = BridgeClient() + + + for policy in ensemble: + policy.start_episode() + obs_dict, _ = env.reset() + print("obs_dict keys : ", obs_dict.keys()) + traj = dict(actions=[], obs=[], next_obs=[], sub_obs=[], + uncertainties=[], min_actions=[], max_actions=[], + time_taken=[], joint_pos=[], recovery=[], failure=[], grasp=[], appr=[], diff=[]) + + ###### SETUP SWITCHING LOGIC #### + switchingLogic = SwitchingLogic(parameters, horizon=horizon) + certain_joint_positions = [] + + ###### SET UP RECOVERY #### + use_recovery=False + recovery_activated_during_rollout = 0 + print("rollout recovery enabled ? : ", use_recovery) + + ##### RECOVERY CONFIG #### + recovery_cooldown_duration = 300 # timesteps + recovery_cooldown_timer = recovery_cooldown_duration + recovery_cooldown_active = False + recovery = [] + recovery_steps = 0 + max_recovery_steps = 500 # Safety limit for recovery mode + + ##### CONFIG RECOVERY CONTROLLER #### + backup_controller = BackupController(env, device, tasktype="lift") + state_guess = 0 + last_state = 0 + recovery_mode = False + object_knocked = False + + prev_actions = None # hold previous action + prev_gripper_action = None + + default_quat = torch.tensor([[0.65538, -0.32298, 0.6574, -0.18433]], device=device) #xyzw + # Define the observation keys the trained model expects + # These must match the keys used during training (from shape_metadata) + TRAINED_OBS_KEYS = ['eef_pos', 'eef_quat', 'gripper_pos', 'joint_pos', 'joint_vel', + 'object_position', 'target_object_position', 'actions'] + + print(f'Running for horizon {horizon}') + # print(f"Syncing robot to simulation...") + # if bridge is not None: + # # Get simulated robot initial EE pose + # try: + # # We use the observation manager to get the tracked end-effector pose + # # Based on your environment info, these keys are available in the 'policy' group + # init_obs = obs_dict["policy"] + # + # # eef_pos is (num_envs, 3), eef_quat is (num_envs, 4) + # eef_pos = init_obs["eef_pos"][0].cpu().numpy() + # eef_quat = init_obs["eef_quat"][0].cpu().numpy() # [qw, qx, qy, qz] + # + # # ROS expects [qx, qy, qz, qw], Isaac uses [qw, qx, qy, qz] + # sim_init_pose = [ + # float(eef_pos[0]), float(eef_pos[1]), float(eef_pos[2]), + # float(eef_quat[1]), float(eef_quat[2]), float(eef_quat[3]), float(eef_quat[0]) + # ] + # print(f"Simulated robot initial pose: {sim_init_pose}") + # # bridge.init_real(sim_init_pose) + # except Exception as e: + # print(f"[WARN] Could not retrieve sim initial pose from observations: {e}") + # print("[INFO] Skipping real robot synchronization.") + for i in range(horizon): + # Prepare observations + # print("obs_dict keys : ", obs_dict.keys()) + obs = copy.deepcopy(obs_dict["policy"]) + sub_obs = copy.deepcopy(obs_dict["subtask_terms"]) + + for ob in obs: + obs[ob] = torch.squeeze(obs[ob]) + + for subob in sub_obs: + sub_obs[subob] = torch.squeeze(sub_obs[subob]) + traj["obs"].append(obs) + traj["sub_obs"].append(sub_obs) + + # Check if environment image observations + if hasattr(env.cfg, "image_obs_list"): + # Process image observations for robomimic inference + for image_name in env.cfg.image_obs_list: + if image_name in obs_dict["policy"].keys(): + # Convert from chw uint8 to hwc normalized float + image = torch.squeeze(obs_dict["policy"][image_name]) + image = image.permute(2, 0, 1).clone().float() + image = image / 255.0 + image = image.clip(0.0, 1.0) + obs[image_name] = image + # eef_pos is (num_envs, 3), eef_quat is (num_envs, 4) + eef_pos = obs["eef_pos"].cpu().numpy() + eef_quat = obs["eef_quat"].cpu().numpy() # [qw, qx, qy, qz] + + # ROS expects [qx, qy, qz, qw], Isaac uses [qw, qx, qy, qz] + sim_init_pose = [ + float(eef_pos[0]), float(eef_pos[1]), float(eef_pos[2]), + float(eef_quat[1]), float(eef_quat[2]), float(eef_quat[3]), float(eef_quat[0]) + ] + # print(f"Simulated robot pose: {sim_init_pose}") + + # Filter observations to only include keys the model was trained with + # This prevents dimension mismatch errors when env has extra observations + policy_obs = {k: obs[k] for k in TRAINED_OBS_KEYS if k in obs} + + #### get policy action #### + metrics = ensemble_uncertainty(ensemble, policy_obs) + uncertainty = metrics['variance'] + #print(f"Uncdertainty : {uncertainty}") + policy_actions = metrics['mean'] + diff=metrics['diff'] + + # Robust state estimation based on actual observations + is_grasping = bool(sub_obs['grasp'].item()) if sub_obs['grasp'].numel() == 1 else bool(torch.any(sub_obs['grasp'])) + + # Update state guess based on actual gripper/object state + if is_grasping: + # Object is grasped - should be in LIFT or later states + if state_guess < 4: # LIFT_OBJECT = 4 + state_guess = 4 + print(f"[STATE] Updated to LIFT_OBJECT (grasp detected)") + else: + # Not grasping - if we thought we were lifting, we dropped it + if state_guess >= 4 and state_guess < 7: # Was in lift/move states + # Object dropped - need to re-approach + state_guess = 0 # Go back to REST to safely re-approach + #print(f"[STATE] Reset to REST (grasp lost), activating recovery") + # recovery_mode= True + + sm_actions, state_guess = backup_controller.get_controller_action(state_guess, 0) + if last_state != state_guess: + print(f"[STATE] Transition: {last_state} -> {state_guess}") + last_state = state_guess + + + # this is the switching logic + uncertainty_triggered = switchingLogic.check(uncertainty, i) + if uncertainty_triggered: + # uncertainty triggered + if use_recovery and not recovery_cooldown_active: + # if we have switched to recovery + if not recovery_mode: + recovery_mode = True + recovery_activated_during_rollout+=1 + recovery_steps = 0 + print(f"[RECOVERY] Step {i} - Starting recovery from state {state_guess}") + + if recovery_mode: + #print(f"Recovery Actions") + actions = sm_actions + recovery_steps += 1 + + # Exit recovery when task completes or after max steps + # max_recovery_steps = 500 + # if recovery_steps > max_recovery_steps: # APPROACH_GOAL = 7 + # print(f"[RECOVERY] Completed after {recovery_steps} steps, state={state_guess}") + # recovery_mode = False + # recovery_cooldown_active = True + # recovery_cooldown_timer = recovery_cooldown_duration + else: + # Decrement cooldown when not in recovery + # if recovery_cooldown_active: + # recovery_cooldown_timer -= 1 + # if recovery_cooldown_timer <= 0: + # recovery_cooldown_active = False + # recovery_cooldown_timer = recovery_cooldown_duration + # print("Recovery Cooldown Ended") + + actions = policy_actions + + # Unnormalize actions + if args_cli.norm_factor_min is not None and args_cli.norm_factor_max is not None: + actions = ( + (actions + 1) * (args_cli.norm_factor_max - args_cli.norm_factor_min) + ) / 2 + args_cli.norm_factor_min + actions = actions.to(device=device).view(1, env.action_space.shape[1]) + + # print(f"absolute pose: {sim_init_pose}") + # Apply actions + obs_dict, _, terminated, truncated, _ = env.step(actions) + recovery.append(recovery_mode) + # update obs from taking action + obs = obs_dict["policy"] + sub_obs = obs_dict["subtask_terms"] + + # Compute absolute pose for the ROS bridge + with torch.no_grad(): + # Get latest observations from the 'policy' group + #obs_dct = env.observation_manager.compute_group("policy") + eef_pos = obs["eef_pos"] # (num_envs, 3) + eef_quat = obs["eef_quat"] # (num_envs, 4) -> [qw, qx, qy, qz] + + # Calculate absolute target for bridge (current + delta) + # actions[:, 0:3] is the position delta + abs_pos = eef_pos + actions[:, 0:3] + + # For orientation, we map Isaac [qw, qx, qy, qz] to ROS [qx, qy, qz, qw] + # and keep the current orientation (or integrate if needed, but simple is better) + abs_quat_ros = torch.cat([eef_quat[:, 1:], eef_quat[:, 0:1]], dim=-1) + #abs_quat_ros = default_quat + # Construct a 7-DOF absolute pose [x, y, z, qx, qy, qz, qw] + abs_pose_bridge = torch.cat([abs_pos, abs_quat_ros], dim=-1) + + # print(f"absolute pose action: {abs_pose_bridge}") + # ROS 2 Synchronization via Bridge + if bridge is not None: + # only send new pose every 5 + + # print(f"Trying to publish...") + # Send absolute pose to bridge + bridge.publish_command(abs_pose_bridge[0]) + # Publish Joint States for MoveIt 2 + bridge.publish_joints(obs['abs_joint_pos'][0]) + # print(f"Published joint states: {obs['abs_joint_pos'][0]}") + + # Handle gripper toggling + curr_gripper = obs['gripper_pos'] + # print(f"Grippper pos : {curr_gripper}, left {curr_gripper[0,0]}") + if curr_gripper[0,0] > 0.039: + gripper_action = 1 + if gripper_action != prev_gripper_action: + print(f"DEBUG: Triggering Open Gripper (act={curr_gripper})") + bridge.open_gripper() + else: + gripper_action=0 + if gripper_action!=prev_gripper_action: + print(f"DEBUG: Triggering Close Gripper (act={curr_gripper})") + bridge.close_gripper() + prev_gripper_action = gripper_action + + + + # Slow down the loop to ~1Hz + time.sleep(0.2) + + # Record trajectory + traj["actions"].append(actions.tolist()) + traj["next_obs"].append(obs) + traj['uncertainties'].append(uncertainty) + traj['max_actions'].append(metrics['max']) + traj['min_actions'].append(metrics['min']) + traj['time_taken'].append(metrics['time_taken']) + traj['joint_pos'].append(obs['abs_joint_pos']) + traj['recovery'].append(recovery) + traj['diff'].append(diff) + if torch.any(obs['object_knocked']): + print("[MONITOR] object knocked over") + object_knocked = True + + # Check if rollout was successful + if bool(success_term.func(env, **success_term.params)[0]): + print(f"DEBUG: Triggering Open Gripper (act={curr_gripper})") + bridge.open_gripper() + traj['failure'].append(" ") + grasp = torch.any(sub_obs['grasp']) + traj['grasp'].append(grasp.item()) + #appr = torch.any(sub_obs['stacked']) + appr = torch.any(sub_obs['appr_goal']) + traj['appr'].append(appr.item()) + print(f"grasp : {traj['grasp']}, appr : {traj['appr']}") + # print(f"grasp : {grasp.item()}, appr : {appr.item()}") + return True, traj, recovery_activated_during_rollout, "" + + elif terminated or truncated: + print(f"DEBUG: Triggering Open Gripper (act={curr_gripper})") + bridge.open_gripper() + grasp = torch.any(sub_obs['grasp']) + traj['grasp'].append(grasp.item()) + #appr = torch.any(sub_obs['stacked']) + appr = torch.any(sub_obs['appr_goal']) + traj['appr'].append(appr.item()) + #print(f"grasp : {traj['grasp']}, appr : {traj['appr']}") + if i==horizon: + #if the failure was caused by object collision + traj['failure'].append("timeout") + failure_reason = "timeout" + else: + #if failure due to timeout, record timeout + traj['failure'].append("knocked") + failure_reason = "object_knocked" + return False, traj, recovery_activated_during_rollout, failure_reason + prev_actions = actions + + print(f"DEBUG: Triggering Open Gripper (act={curr_gripper})") + bridge.open_gripper() + #print("REcovery : ", traj["recovery"]) + traj['failure'].append("timeout") + grasp = torch.any(sub_obs['grasp']) + traj['grasp'].append(grasp.item()) + #appr = torch.any(sub_obs['stacked']) + appr = torch.any(sub_obs['appr_goal']) + traj['appr'].append(appr.item()) + return False, traj, recovery_activated_during_rollout, "" + + +def load_ensemble(device, ensemble_path): + """Load an ensemble of policies from checkpoint paths. + + Updated for robomimic v0.5 compatibility. + """ + with open(ensemble_path, 'r') as file: + ensemble_paths = [path.strip() for path in file.readlines()] + ensemble = [] + for path in ensemble_paths: + if not path: # Skip empty lines + continue + policy, _ = policy_from_checkpoint_v05(ckpt_path=path, device=device, verbose=True) + ensemble.append(policy) + print(f"[DEBUG] loaded {len(ensemble)} policies") + return ensemble + + +def clear_files(*paths : str): + if len(paths) < 1: + raise ValueError("At least one path is required.") + if os.path.exists(paths[0]): + #overwrite = input(f"You are about to overwrite rollout data at: {paths}\nContinue (y/n):") + overwrite = "y" + match overwrite.lower(): + case 'y' | 'Y': + for path in paths: + with open(path, 'w') as file: + pass + case _: + exit() + + +class BridgeClient: + def __init__(self, port=5005): + self.sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) + self.addr = ('127.0.0.1', port) + self.sock.settimeout(0.5) + self._last_print_time = 0 + + def publish_command(self, actions): + """Publish 7-DOF Cartesian target: [x, y, z, qx, qy, qz, qw]""" + if hasattr(actions, 'cpu'): + act_list = actions.cpu().numpy().flatten().tolist() + else: + act_list = np.array(actions).flatten().tolist() + + if len(act_list) < 7: return + + payload = {'type': 'cartesian', 'data': act_list[:7]} + self.sock.sendto(json.dumps(payload).encode(), self.addr) + + # Throttled debug print (every 1 second) + curr_time = time.time() + if curr_time - self._last_print_time > 1.0: + # print(f"DEBUG: Sent Cartesian to Bridge: {act_list}") + self._last_print_time = curr_time + + def publish_joints(self, positions): + """Publish 7-DOF Joint positions""" + if hasattr(positions, 'cpu'): + pos_list = positions.cpu().numpy().flatten().tolist() + else: + pos_list = np.array(positions).flatten().tolist() + + if len(pos_list) < 7: return + + payload = {'type': 'joint', 'data': pos_list[:7]} + self.sock.sendto(json.dumps(payload).encode(), self.addr) + + # Throttled debug print (every 1s) + curr_time = time.time() + if curr_time - self._last_print_time > 1.0: + # print(f"DEBUG: Sent Joint Target to Bridge: {[round(p, 3) for p in pos_list[:7]]}") + self._last_print_time = curr_time + + def open_gripper(self): + print("DEBUG: Sending Open Gripper to Bridge") + payload = {'type': 'gripper', 'width': 0.08} + self.sock.sendto(json.dumps(payload).encode(), self.addr) + + def close_gripper(self): + print("DEBUG: Sending Close Gripper to Bridge") + payload = {'type': 'gripper', 'width': 0.061} + self.sock.sendto(json.dumps(payload).encode(), self.addr) + + def get_robot_state(self): + payload = {'type': 'query'} + self.sock.sendto(json.dumps(payload).encode(), self.addr) + try: + data, _ = self.sock.recvfrom(2048) + return json.loads(data.decode()) + except socket.timeout: + return None + + def init_real(self, sim_pose): + print("[INFO] Initializing real robot synchronization via bridge...") + self.publish_command(sim_pose) + target_pos = np.array(sim_pose[:3]) + + while True: + state = self.get_robot_state() + if state and state.get('real_pose'): + real_pos = np.array(state['real_pose']['pos']) + dist = np.linalg.norm(target_pos - real_pos) + print(f"[INFO] Distance to target: {dist:.4f}m") + if dist < 0.05: + print("[INFO] Real robot synchronized with simulation!") + break + else: + print("[INFO] Waiting for bridge on 127.0.0.1:5005...") + time.sleep(0.5) + + +def main(): + """Run a trained policy from robomimic with Isaac Lab environment.""" + # parse configuration + env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=1, use_fabric=not args_cli.disable_fabric) + + # Set observations to dictionary mode for Robomimic + env_cfg.observations.policy.concatenate_terms = False + + #create a log handler + # loghelper = LoggingHelper() + + + + # Disable recorder + env_cfg.recorders = None + + # Extract success checking function + success_term = env_cfg.terminations.success + env_cfg.terminations.success = None + + # Get timeout signal + timeout = env_cfg.terminations.time_out + #env_cfg.terminations.time_out = None + + # Create environment + env = gym.make(args_cli.task, cfg=env_cfg).unwrapped + + # Set seed + torch.manual_seed(args_cli.seed) + ##### unset these two lines for deterministic randomness + # np.random.seed(args_cli.seed) + # random.seed(args_cli.seed) + + env.seed(args_cli.seed) + + # Acquire device + device = TorchUtils.get_torch_device(try_to_use_cuda=True) + + # load the stack cube ensemble + # stack_cube_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/stack_cube_ensemble.txt') + + #pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/ensembles.txt') + #pick_place_ensemble = load_ensemble(device, ensemble_path='docs/place/low/Dev-IK-Rel-Place-v0/best_models/best_model_paths.txt') + # pick_place_ensemble_30 = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/pick_place_ensemble_30_paths.txt') + pick_place_ensemble = load_ensemble(device, ensemble_path='docs/lift/Dev-IK-Rel-v1/best_models/best_model_paths.txt') + + # pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/insert_paths.txt') + #pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/inser_top_paths.txt') + + # Lets set these to the 0.99 confidence + parameters = { + 'beaker_lift':{ + 0 : { + "confidence_level": 7.716635000000001e-05, + "window_size": 10, + "max_peaks": 8 + }, + 1 : { + "confidence_level": 7.712965000000002e-05, + "window_size": 10, + "max_peaks": 8 + }, + 2 : { + "confidence_level": 7.057612000000003e-05, + "window_size": 10, + "max_peaks": 8 + }, + 3 : { + "confidence_level": 0.00010471817000000003, + "window_size": 10, + "max_peaks": 8 + }, + 4 : { + "confidence_level": 0.00010728666000000004, + "window_size": 10, + "max_peaks": 8 + }, + 5 : { + "confidence_level": 0.00011272453000000001, + "window_size": 10, + "max_peaks": 8 + }, + 6 : { + "confidence_level": 1.6e-08, + "window_size": 10, + "max_peaks": 8 + }, + }, + + 'vial_insert': { + 0 : { + "confidence_level": 0.00016269120400000059, + "window_size": 9, + "max_peaks": 8 + }, + 1 : { + "confidence_level": 0.000173801014, + "window_size": 9, + "max_peaks": 8 + }, + 2 : { + "confidence_level": 0.00018948240900000074, + "window_size": 9, + "max_peaks": 8 + }, + 3 : { + "confidence_level": 0.0007136359360000004, + "window_size": 9, + "max_peaks": 8 + }, + 4 : { + "confidence_level": 0.00015286798600000018, + "window_size": 9, + "max_peaks": 8 + }, + 5 : { + "confidence_level": 0.00047967177600000013, + "window_size": 9, + "max_peaks": 8 + }, + 6 : { + "confidence_level": 0.26672138987099997, + "window_size": 9, + "max_peaks": 8 + }, + + }, + 'force_backup': { + 0 : { + "confidence_level": 0.0, + "window_size": 9, + "max_peaks": 8 + }, + 1 : { + "confidence_level": 0.00, + "window_size": 9, + "max_peaks": 8 + }, + 2 : { + "confidence_level": 0.000, + "window_size": 9, + "max_peaks": 8 + }, + 3 : { + "confidence_level": 0.00, + "window_size": 9, + "max_peaks": 8 + }, + 4 : { + "confidence_level": 0.000, + "window_size": 9, + "max_peaks": 8 + }, + 5 : { + "confidence_level": 0.00, + "window_size": 9, + "max_peaks": 8 + }, + 6 : { + "confidence_level": 0.0, + "window_size": 9, + "max_peaks": 8 + }, + } + } + + + + model_arch = "BC_RNN_GMM" + task = "pick_place" # stack_cube or pick_place + model_name = f"ensemble" + number = 'no_recovery_video' + + results_path = f"docs/training_data/{task}/uncertainty_rollout_{task}/{model_name}/run_{number}" + + uncertainties_path = f"{results_path}/uncertainties{number}.txt" + actions_path = f"{results_path}/actions{number}.txt" + min_actions_path = f"{results_path}/min_actions{number}.txt" + max_actions_path = f"{results_path}/max_actions{number}.txt" + time_taken_path = f"{results_path}/time_taken{number}.txt" + recovery_activated_during_rollout_path = f"{results_path}/recovery_activated_during_rollout.txt" + rollout_uncert_path = f"{uncertainties_path}/rollout_uncerts" + rollout_log_path = f"{uncertainties_path[:len(uncertainties_path)-4]}_rollout_log.txt" + + rollout_log_path = f"{uncertainties_path[:len(uncertainties_path)-4]}_rollout_log.txt" # remove the '.txt' and add rollout_log.txt + + uncertainty_results_path = f"{results_path}/uncertainty_plots" + trajectory_results_path = f"{results_path}/trajectory_plots" + os.makedirs(uncertainty_results_path, exist_ok=True) + os.makedirs(trajectory_results_path, exist_ok=True) + + + # clear files + clear_files( + uncertainties_path, actions_path, + min_actions_path, max_actions_path, + time_taken_path, recovery_activated_during_rollout_path + ) + + # Run policy + results = [] + failed_no_intervention =[] + intervention = [] + intervention_success = [] + knocked=0 + intervention_failure = [] + for trial in range(args_cli.num_rollouts): + print(f"[INFO] Starting trial {trial}") + + terminated, traj, recovery_activated_during_rollout, failure = rollout_ensemble(pick_place_ensemble[:args_cli.ensemble_size], env, success_term, args_cli.horizon, device, parameters['beaker_lift'], use_recovery=args_cli.use_recovery, rollout_num=trial) + # save the uncertainties + print("Finished rollout, recovery needed : ", recovery_activated_during_rollout) + #print("actions shape : ", traj['actions']) + + + + results.append(terminated) + if not terminated: + if recovery_activated_during_rollout <=0: + failed_no_intervention.append(1) + + if recovery_activated_during_rollout > 0: + intervention.append(1) + if terminated: + intervention_success.append(1) + else: + intervention_failure.append(1) + else: + intervention.append(0) + if "knocked" in traj['failure'] or (len(traj['failure']) > 0 and traj['failure'][-1] == "knocked"): + knocked+=1 + + print(f'Current success rate: {results.count(True)/len(results):.2%}') + print(f'Current intervention rate: {sum(intervention)/len(intervention):.2%} ({sum(intervention)}/{len(intervention)})') + print(f"[INFO] Trial {trial}: {'SUCCESS' if terminated else 'FAILED'}\n") + #loghelper.stopEpoch(trial) + + # Prepare summary text + exp_name = args_cli.exp_name if args_cli.exp_name else "unnamed" + summary_text = f"\nSummary for experiment {exp_name}\n" + summary_text += f"\nSuccessful trials: {results.count(True)}, out of {len(results)} trials\n" + summary_text += f"Success rate: {results.count(True) / len(results)}\n" + summary_text += f'Failed runs with no intervention : {sum(failed_no_intervention)}\n' + summary_text += f"Intervention rate : {sum(intervention)/len(intervention)}. Of which success : {sum(intervention_success)}, of which failed {sum(intervention_failure)}\n" + summary_text += f"Intervention Results: {intervention}\n" + summary_text += f"Number of times object knocked over : {knocked}\n" + summary_text += f"Trial Results: {results}\n\n" + summary_text += f"Environment random seed: {args_cli.seed}\n" + + # Print to console + print(summary_text) + + # Write to file + # results_file = "docs/ensemble_run_results.txt" + # with open(results_file, 'a') as f: + # f.write(summary_text) + # f.write("\n" + "="*80 + "\n\n") + # print(f"Results appended to: {results_file}") + + # if franka_gripper is not None: + # franka_gripper.destroy_node() + # if franka_arm is not None: + # franka_arm.destroy_node() + if rclpy.ok(): + rclpy.shutdown() + env.close() + + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/imitation_learning/robomimic/play_ensemble_ros2_backup.py b/scripts/imitation_learning/robomimic/play_ensemble_ros2_backup.py new file mode 100644 index 000000000000..ec19d1582e9b --- /dev/null +++ b/scripts/imitation_learning/robomimic/play_ensemble_ros2_backup.py @@ -0,0 +1,924 @@ +# Copyright (c) 2022-2025, The Isaac Lab Project Developers. +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause + +# run this script: +# ./isaaclab.sh -p scripts/imitation_learning/robomimic/play_ensemble_v05.py --device cuda --task Dev-IK-Rel-v0 --num_rollouts 100 +# stack cube task: ./isaaclab.sh -p scripts/imitation_learning/robomimic/play.py --device cuda --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --num_rollouts 50 +"""Script to play and evaluate a trained policy from robomimic. + +This script loads a robomimic policy and plays it in an Isaac Lab environment. +Updated for robomimic v0.5 compatibility. + +Args: + task: Name of the environment. + checkpoint: Path to the robomimic policy checkpoint. + horizon: If provided, override the step horizon of each rollout. + num_rollouts: If provided, override the number of rollouts. + seed: If provided, overeride the default random seed. + norm_factor_min: If provided, minimum value of the action space normalization factor. + norm_factor_max: If provided, maximum value of the action space normalization factor. +""" + +"""Launch Isaac Sim Simulator first.""" + + +import argparse + +from isaaclab.app import AppLauncher + +# add argparse arguments +parser = argparse.ArgumentParser(description="Evaluate robomimic policy for Isaac Lab environment.") +parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.") +parser.add_argument("--video_length", type=int, default=800, help="Length of the recorded video (in steps).") +parser.add_argument( + "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations." +) +parser.add_argument("--task", type=str, default=None, help="Name of the task.") +parser.add_argument("--checkpoint", type=str, default=None, help="Pytorch model checkpoint to load.") +parser.add_argument("--horizon", type=int, default=2000, help="Step horizon of each rollout.") +parser.add_argument("--num_rollouts", type=int, default=1, help="Number of rollouts.") +parser.add_argument("--seed", type=int, default=101, help="Random seed.") +parser.add_argument( + "--norm_factor_min", type=float, default=None, help="Optional: minimum value of the normalization factor." +) +parser.add_argument( + "--norm_factor_max", type=float, default=None, help="Optional: maximum value of the normalization factor." +) +parser.add_argument("--enable_pinocchio", default=False, action="store_true", help="Enable Pinocchio.") + +parser.add_argument("--model_name", type=str, default=None) +parser.add_argument("--exp_name", type=str, default=None) + +group = parser.add_mutually_exclusive_group() +group.add_argument("--use_recovery", type=bool ,default= False, help="Enable recovery mechanism. By default recovery is enabled.") + +parser.add_argument("--ensemble_size", type=int, default=10) + +# append AppLauncher cli args +AppLauncher.add_app_launcher_args(parser) +# parse the arguments +args_cli = parser.parse_args() + +if args_cli.enable_pinocchio: + # Import pinocchio before AppLauncher to force the use of the version installed by IsaacLab and not the one installed by Isaac Sim + # pinocchio is required by the Pink IK controllers and the GR1T2 retargeter + import pinocchio # noqa: F401 +import numpy as np +# launch omniverse app +app_launcher = AppLauncher(args_cli) +simulation_app = app_launcher.app + +"""Rest everything follows.""" + +import time +import copy +import random +import os +import gymnasium as gym +import torch +import math +from collections import deque + +import robomimic.utils.file_utils as FileUtils +import robomimic.utils.torch_utils as TorchUtils +from isaaclab_tasks.manager_based.manipulation.cube_lift.mdp.observations import get_joint_pos, object_grasped +#from chills.tasks.mdp.observations import get_joint_pos, object_grasped +if args_cli.enable_pinocchio: + import isaaclab_tasks.manager_based.manipulation.pick_place # noqa: F401 + +from isaaclab_tasks.utils import parse_env_cfg +#from isaaclab.utils.logging_helper import LoggingHelper, ErrorType, LogType +from isaaclab.utils.math import matrix_from_quat, convert_quat, quat_mul, euler_xyz_from_quat +from evaluation import ensemble_uncertainty +from isaaclab.safety.switchingLogic import SwitchingLogic +from isaaclab.controllers import DifferentialIKController, DifferentialIKControllerCfg +from isaaclab.utils.math import subtract_frame_transforms, euler_xyz_from_quat +from isaaclab.managers import SceneEntityCfg +from isaaclab.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg, BinaryJointPositionActionCfg +from isaaclab.envs.mdp.actions.binary_joint_actions import BinaryJointPositionAction +from backup_controller_handler import BackupController + +# --- ROS2 imports --- +# --- UDP Bridge imports --- +import socket +import json +import time +import numpy as np + +def policy_from_checkpoint_v05(ckpt_path, device, verbose=True): + """ + Load a policy from a checkpoint file - compatible with robomimic v0.5. + + This is a wrapper that handles API differences between robomimic versions. + + Args: + ckpt_path: Path to the checkpoint file. + device: Device to load the policy on. + verbose: Whether to print verbose output. + + Returns: + policy: The loaded policy. + ckpt_dict: The checkpoint dictionary. + """ + import robomimic + from packaging import version + + # Check robomimic version + robomimic_version = getattr(robomimic, '__version__', '0.5.0') + + if verbose: + print(f"[INFO] Loading checkpoint with robomimic version {robomimic_version}") + + # robomimic v0.5+ may have different API + if version.parse(robomimic_version) >= version.parse('0.5.0'): + # v0.5 API - policy_from_checkpoint returns (policy, ckpt_dict) + try: + result = FileUtils.policy_from_checkpoint(ckpt_path=ckpt_path, device=device, verbose=verbose) + if isinstance(result, tuple): + return result + else: + return result, None + except TypeError as e: + if verbose: + print(f"[WARN] policy_from_checkpoint failed with: {e}") + print("[INFO] Trying alternative loading method...") + + # Alternative: manual loading + import torch + import json + from robomimic.algo import algo_factory + from robomimic.config import config_factory + + ckpt_dict = torch.load(ckpt_path, map_location=device) + + # Get config from checkpoint + config = ckpt_dict.get('config', {}) + if isinstance(config, dict): + algo_name = config.get('algo_name', 'bc') + config_obj = config_factory(algo_name) + with config_obj.values_unlocked(): + config_obj.update(config) + config = config_obj + + # Create policy + policy = algo_factory( + algo_name=config.algo_name, + config=config, + obs_key_shapes=ckpt_dict.get('shape_metadata', {}).get('all_shapes', {}), + ac_dim=ckpt_dict.get('shape_metadata', {}).get('ac_dim', 7), + device=device, + ) + + # Load weights + policy.deserialize(ckpt_dict['model']) + policy.set_eval() + + return policy, ckpt_dict + else: + # Older API + return FileUtils.policy_from_checkpoint(ckpt_path=ckpt_path, device=device, verbose=verbose) + + + +# tip over detection +def has_fallen(asset, threshold: float = -0.7, debug: bool = True) -> bool: + """ + Detect if an IsaacLab asset has fallen over by checking its up vector. + + Args: + asset: IsaacLab asset (e.g. robot, object) with .get_world_pose() + threshold: minimum z-component of the up vector (1.0 = perfectly upright). + Typical cutoff ~0.7 (≈45° tilt). + debug: if True, prints internal values for inspection. + + Returns: + True if fallen, False otherwise. + """ + quat = asset.data.body_link_quat_w # (pos, quat), usually torch.Tensors + # print("Beaker quat : ", quat) + # Ensure torch tensor, shape (4,) + if isinstance(quat, (list, tuple)): + quat = torch.tensor(quat, dtype=torch.float32) + quat = quat.view(-1, 4) # shape (1,4) + + # Convert quaternion to wxyz ordering + quat_wxyz = convert_quat(quat, to="wxyz") + + # Rotation matrix (1,3,3) + R = matrix_from_quat(quat_wxyz) + + # World up vector is the third column of rotation matrix + up = R[0, :, 2] # shape (3,) + uprightness = float(up[2]) + + if debug: + print(f"[has_fallen] quat={quat_wxyz.cpu().numpy().round(4)}") + print(f"[has_fallen] up={up.cpu().numpy().round(4)} uprightness={uprightness:.3f}") + + return uprightness > threshold + +def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, use_recovery=True, rollout_num=0): + """Perform a single rollout of the policy in the environment. + + Args: + policy: The robomimicpolicy to play. + env: The environment to play in. + horizon: The step horizon of each rollout. + device: The device to run the policy on. + + Returns: + terminated: Whether the rollout terminated. + traj: The trajectory of the rollout. + """ + ## do setup + print("running rollout number : ", rollout_num) + # Initialize UDP Bridge Client + bridge = BridgeClient() + + + for policy in ensemble: + policy.start_episode() + obs_dict, _ = env.reset() + print("obs_dict keys : ", obs_dict.keys()) + traj = dict(actions=[], obs=[], next_obs=[], sub_obs=[], + uncertainties=[], min_actions=[], max_actions=[], + time_taken=[], joint_pos=[], recovery=[], failure=[], grasp=[], appr=[], diff=[]) + + ###### SETUP SWITCHING LOGIC #### + switchingLogic = SwitchingLogic(parameters, horizon=horizon) + certain_joint_positions = [] + + ###### SET UP RECOVERY #### + use_recovery=True + recovery_activated_during_rollout = 0 + print("rollout recovery enabled ? : ", use_recovery) + + ##### RECOVERY CONFIG #### + recovery_cooldown_duration = 300 # timesteps + recovery_cooldown_timer = recovery_cooldown_duration + recovery_cooldown_active = False + recovery = [] + recovery_steps = 0 + max_recovery_steps = 500 # Safety limit for recovery mode + + ##### CONFIG RECOVERY CONTROLLER #### + backup_controller = BackupController(env, device, tasktype="insert_top") + state_guess = 0 + last_state = 0 + recovery_mode = False + object_knocked = False + + prev_actions = None # hold previous action + prev_gripper_action = None + + default_quat = torch.tensor([[0.65538, -0.32298, 0.6574, -0.18433]], device=device) #xyzw + # Define the observation keys the trained model expects + # These must match the keys used during training (from shape_metadata) + TRAINED_OBS_KEYS = ['eef_pos', 'eef_quat', 'gripper_pos', 'joint_pos', 'joint_vel', + 'object_position', 'target_object_position', 'actions'] + + print(f'Running for horizon {horizon}') + # print(f"Syncing robot to simulation...") + # if bridge is not None: + # # Get simulated robot initial EE pose + # try: + # # We use the observation manager to get the tracked end-effector pose + # # Based on your environment info, these keys are available in the 'policy' group + # init_obs = obs_dict["policy"] + # + # # eef_pos is (num_envs, 3), eef_quat is (num_envs, 4) + # eef_pos = init_obs["eef_pos"][0].cpu().numpy() + # eef_quat = init_obs["eef_quat"][0].cpu().numpy() # [qw, qx, qy, qz] + # + # # ROS expects [qx, qy, qz, qw], Isaac uses [qw, qx, qy, qz] + # sim_init_pose = [ + # float(eef_pos[0]), float(eef_pos[1]), float(eef_pos[2]), + # float(eef_quat[1]), float(eef_quat[2]), float(eef_quat[3]), float(eef_quat[0]) + # ] + # print(f"Simulated robot initial pose: {sim_init_pose}") + # # bridge.init_real(sim_init_pose) + # except Exception as e: + # print(f"[WARN] Could not retrieve sim initial pose from observations: {e}") + # print("[INFO] Skipping real robot synchronization.") + for i in range(horizon): + # Prepare observations + print("obs_dict keys : ", obs_dict.keys()) + obs = copy.deepcopy(obs_dict["policy"]) + sub_obs = copy.deepcopy(obs_dict["subtask_terms"]) + + for ob in obs: + obs[ob] = torch.squeeze(obs[ob]) + + for subob in sub_obs: + sub_obs[subob] = torch.squeeze(sub_obs[subob]) + traj["obs"].append(obs) + traj["sub_obs"].append(sub_obs) + + # Check if environment image observations + if hasattr(env.cfg, "image_obs_list"): + # Process image observations for robomimic inference + for image_name in env.cfg.image_obs_list: + if image_name in obs_dict["policy"].keys(): + # Convert from chw uint8 to hwc normalized float + image = torch.squeeze(obs_dict["policy"][image_name]) + image = image.permute(2, 0, 1).clone().float() + image = image / 255.0 + image = image.clip(0.0, 1.0) + obs[image_name] = image + # eef_pos is (num_envs, 3), eef_quat is (num_envs, 4) + eef_pos = obs["eef_pos"].cpu().numpy() + eef_quat = obs["eef_quat"].cpu().numpy() # [qw, qx, qy, qz] + + # ROS expects [qx, qy, qz, qw], Isaac uses [qw, qx, qy, qz] + sim_init_pose = [ + float(eef_pos[0]), float(eef_pos[1]), float(eef_pos[2]), + float(eef_quat[1]), float(eef_quat[2]), float(eef_quat[3]), float(eef_quat[0]) + ] + print(f"Simulated robot pose: {sim_init_pose}") + + # Filter observations to only include keys the model was trained with + # This prevents dimension mismatch errors when env has extra observations + policy_obs = {k: obs[k] for k in TRAINED_OBS_KEYS if k in obs} + + #### get policy action #### + metrics = ensemble_uncertainty(ensemble, policy_obs) + uncertainty = metrics['variance'] + #print(f"Uncdertainty : {uncertainty}") + policy_actions = metrics['mean'] + diff=metrics['diff'] + + # Robust state estimation based on actual observations + is_grasping = bool(sub_obs['grasp'].item()) if sub_obs['grasp'].numel() == 1 else bool(torch.any(sub_obs['grasp'])) + + # Update state guess based on actual gripper/object state + if is_grasping: + # Object is grasped - should be in LIFT or later states + if state_guess < 4: # LIFT_OBJECT = 4 + state_guess = 4 + print(f"[STATE] Updated to LIFT_OBJECT (grasp detected)") + else: + # Not grasping - if we thought we were lifting, we dropped it + if state_guess >= 4 and state_guess < 7: # Was in lift/move states + # Object dropped - need to re-approach + state_guess = 0 # Go back to REST to safely re-approach + #print(f"[STATE] Reset to REST (grasp lost), activating recovery") + # recovery_mode= True + + sm_actions, state_guess = backup_controller.get_controller_action(state_guess, 0) + if last_state != state_guess: + print(f"[STATE] Transition: {last_state} -> {state_guess}") + last_state = state_guess + + + # this is the switching logic + uncertainty_triggered = switchingLogic.check(uncertainty, i) + if uncertainty_triggered: + # uncertainty triggered + if use_recovery and not recovery_cooldown_active: + # if we have switched to recovery + if not recovery_mode: + recovery_mode = True + recovery_activated_during_rollout+=1 + recovery_steps = 0 + print(f"[RECOVERY] Step {i} - Starting recovery from state {state_guess}") + + if recovery_mode: + #print(f"Recovery Actions") + actions = sm_actions + recovery_steps += 1 + + # Exit recovery when task completes or after max steps + # max_recovery_steps = 500 + # if recovery_steps > max_recovery_steps: # APPROACH_GOAL = 7 + # print(f"[RECOVERY] Completed after {recovery_steps} steps, state={state_guess}") + # recovery_mode = False + # recovery_cooldown_active = True + # recovery_cooldown_timer = recovery_cooldown_duration + else: + # Decrement cooldown when not in recovery + # if recovery_cooldown_active: + # recovery_cooldown_timer -= 1 + # if recovery_cooldown_timer <= 0: + # recovery_cooldown_active = False + # recovery_cooldown_timer = recovery_cooldown_duration + # print("Recovery Cooldown Ended") + + actions = policy_actions + + # Unnormalize actions + if args_cli.norm_factor_min is not None and args_cli.norm_factor_max is not None: + actions = ( + (actions + 1) * (args_cli.norm_factor_max - args_cli.norm_factor_min) + ) / 2 + args_cli.norm_factor_min + actions = actions.to(device=device).view(1, env.action_space.shape[1]) + + print(f"absolute pose: {sim_init_pose}") + # Apply actions + obs_dict, _, terminated, truncated, _ = env.step(actions) + recovery.append(recovery_mode) + # update obs from taking action + obs = obs_dict["policy"] + sub_obs = obs_dict["subtask_terms"] + + # Compute absolute pose for the ROS bridge + with torch.no_grad(): + # Get latest observations from the 'policy' group + #obs_dct = env.observation_manager.compute_group("policy") + eef_pos = obs["eef_pos"] # (num_envs, 3) + eef_quat = obs["eef_quat"] # (num_envs, 4) -> [qw, qx, qy, qz] + + # Calculate absolute target for bridge (current + delta) + # actions[:, 0:3] is the position delta + abs_pos = eef_pos + actions[:, 0:3] + + # For orientation, we map Isaac [qw, qx, qy, qz] to ROS [qx, qy, qz, qw] + # and keep the current orientation (or integrate if needed, but simple is better) + abs_quat_ros = torch.cat([eef_quat[:, 1:], eef_quat[:, 0:1]], dim=-1) + #abs_quat_ros = default_quat + # Construct a 7-DOF absolute pose [x, y, z, qx, qy, qz, qw] + abs_pose_bridge = torch.cat([abs_pos, abs_quat_ros], dim=-1) + + print(f"absolute pose action: {abs_pose_bridge}") + # ROS 2 Synchronization via Bridge + if bridge is not None: + + print(f"Trying to publish...") + # Send absolute pose to bridge + bridge.publish_command(abs_pose_bridge[0]) + + # Handle gripper toggling + curr_gripper = float(actions[0, -1]) + if prev_gripper_action is not None: + if curr_gripper != prev_gripper_action: + if curr_gripper > 0: + bridge.open_gripper() + else: + bridge.close_gripper() + prev_gripper_action = curr_gripper + # Slow down the loop to ~1Hz + time.sleep(10.0) + + # Record trajectory + traj["actions"].append(actions.tolist()) + traj["next_obs"].append(obs) + traj['uncertainties'].append(uncertainty) + traj['max_actions'].append(metrics['max']) + traj['min_actions'].append(metrics['min']) + traj['time_taken'].append(metrics['time_taken']) + traj['joint_pos'].append(obs['abs_joint_pos']) + traj['recovery'].append(recovery) + traj['diff'].append(diff) + if torch.any(obs['object_knocked']): + print("[MONITOR] object knocked over") + object_knocked = True + + # Check if rollout was successful + if bool(success_term.func(env, **success_term.params)[0]): + traj['failure'].append(" ") + grasp = torch.any(sub_obs['grasp']) + traj['grasp'].append(grasp.item()) + #appr = torch.any(sub_obs['stacked']) + appr = torch.any(sub_obs['appr_goal']) + traj['appr'].append(appr.item()) + print(f"grasp : {traj['grasp']}, appr : {traj['appr']}") + # print(f"grasp : {grasp.item()}, appr : {appr.item()}") + return True, traj, recovery_activated_during_rollout, "" + + elif terminated or truncated: + grasp = torch.any(sub_obs['grasp']) + traj['grasp'].append(grasp.item()) + #appr = torch.any(sub_obs['stacked']) + appr = torch.any(sub_obs['appr_goal']) + traj['appr'].append(appr.item()) + #print(f"grasp : {traj['grasp']}, appr : {traj['appr']}") + if i==horizon: + #if the failure was caused by object collision + traj['failure'].append("timeout") + failure_reason = "timeout" + else: + #if failure due to timeout, record timeout + traj['failure'].append("knocked") + failure_reason = "object_knocked" + return False, traj, recovery_activated_during_rollout, failure_reason + prev_actions = actions + + + #print("REcovery : ", traj["recovery"]) + traj['failure'].append("timeout") + grasp = torch.any(sub_obs['grasp']) + traj['grasp'].append(grasp.item()) + #appr = torch.any(sub_obs['stacked']) + appr = torch.any(sub_obs['appr_goal']) + traj['appr'].append(appr.item()) + return False, traj, recovery_activated_during_rollout, "" + + +def load_ensemble(device, ensemble_path): + """Load an ensemble of policies from checkpoint paths. + + Updated for robomimic v0.5 compatibility. + """ + with open(ensemble_path, 'r') as file: + ensemble_paths = [path.strip() for path in file.readlines()] + ensemble = [] + for path in ensemble_paths: + if not path: # Skip empty lines + continue + policy, _ = policy_from_checkpoint_v05(ckpt_path=path, device=device, verbose=True) + ensemble.append(policy) + print(f"[DEBUG] loaded {len(ensemble)} policies") + return ensemble + + +def clear_files(*paths : str): + if len(paths) < 1: + raise ValueError("At least one path is required.") + if os.path.exists(paths[0]): + #overwrite = input(f"You are about to overwrite rollout data at: {paths}\nContinue (y/n):") + overwrite = "y" + match overwrite.lower(): + case 'y' | 'Y': + for path in paths: + with open(path, 'w') as file: + pass + case _: + exit() + + +class BridgeClient: + def __init__(self, port=5005): + self.sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) + self.addr = ('127.0.0.1', port) + self.sock.settimeout(0.5) + self._last_print_time = 0 + + def publish_command(self, actions): + """Publish 7-DOF Cartesian target: [x, y, z, qx, qy, qz, qw]""" + if hasattr(actions, 'cpu'): + act_list = actions.cpu().numpy().flatten().tolist() + else: + act_list = np.array(actions).flatten().tolist() + + if len(act_list) < 7: return + + payload = {'type': 'cartesian', 'data': act_list[:7]} + self.sock.sendto(json.dumps(payload).encode(), self.addr) + + # Throttled debug print (every 1 second) + curr_time = time.time() + if curr_time - self._last_print_time > 1.0: + print(f"DEBUG: Sent Cartesian to Bridge: {act_list}") + self._last_print_time = curr_time + + def open_gripper(self): + print("DEBUG: Sending Open Gripper to Bridge") + payload = {'type': 'gripper', 'width': 0.08} + self.sock.sendto(json.dumps(payload).encode(), self.addr) + + def close_gripper(self): + print("DEBUG: Sending Close Gripper to Bridge") + payload = {'type': 'gripper', 'width': 0.0} + self.sock.sendto(json.dumps(payload).encode(), self.addr) + + def get_robot_state(self): + payload = {'type': 'query'} + self.sock.sendto(json.dumps(payload).encode(), self.addr) + try: + data, _ = self.sock.recvfrom(2048) + return json.loads(data.decode()) + except socket.timeout: + return None + + def init_real(self, sim_pose): + print("[INFO] Initializing real robot synchronization via bridge...") + self.publish_command(sim_pose) + target_pos = np.array(sim_pose[:3]) + + while True: + state = self.get_robot_state() + if state and state.get('real_pose'): + real_pos = np.array(state['real_pose']['pos']) + dist = np.linalg.norm(target_pos - real_pos) + print(f"[INFO] Distance to target: {dist:.4f}m") + if dist < 0.05: + print("[INFO] Real robot synchronized with simulation!") + break + else: + print("[INFO] Waiting for bridge on 127.0.0.1:5005...") + time.sleep(0.5) + + +def main(): + """Run a trained policy from robomimic with Isaac Lab environment.""" + # parse configuration + env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=1, use_fabric=not args_cli.disable_fabric) + + # Set observations to dictionary mode for Robomimic + env_cfg.observations.policy.concatenate_terms = False + + #create a log handler + # loghelper = LoggingHelper() + + + + # Disable recorder + env_cfg.recorders = None + + # Extract success checking function + success_term = env_cfg.terminations.success + env_cfg.terminations.success = None + + # Get timeout signal + timeout = env_cfg.terminations.time_out + #env_cfg.terminations.time_out = None + + # Create environment + env = gym.make(args_cli.task, cfg=env_cfg).unwrapped + + # Set seed + torch.manual_seed(args_cli.seed) + ##### unset these two lines for deterministic randomness + # np.random.seed(args_cli.seed) + # random.seed(args_cli.seed) + + env.seed(args_cli.seed) + + # Acquire device + device = TorchUtils.get_torch_device(try_to_use_cuda=True) + + # load the stack cube ensemble + # stack_cube_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/stack_cube_ensemble.txt') + + #pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/ensembles.txt') + #pick_place_ensemble = load_ensemble(device, ensemble_path='docs/place/low/Dev-IK-Rel-Place-v0/best_models/best_model_paths.txt') + # pick_place_ensemble_30 = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/pick_place_ensemble_30_paths.txt') + # pick_place_ensemble = load_ensemble(device, ensemble_path='docs/lift/Dev-IK-Rel-v1/best_models/best_model_paths.txt') + + # pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/insert_paths.txt') + pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/inser_top_paths.txt') + + # Lets set these to the 0.99 confidence + parameters = { + 'beaker_lift' :{ + 0 : { + "confidence_level": 5.373953000000001e-05, + "window_size": 10, + "max_peaks": 8 + }, + 1 : { + "confidence_level": 5.378578e-05, + "window_size": 10, + "max_peaks": 8 + }, + 2 : { + "confidence_level": 6.186318e-05, + "window_size": 10, + "max_peaks": 8 + }, + 3 : { + "confidence_level": 7.819939000000004e-05, + "window_size": 10, + "max_peaks": 8 + }, + 4 : { + "confidence_level": 0.00012073204000000005, + "window_size": 10, + "max_peaks": 8 + }, + 5 : { + "confidence_level": 8.867226000000004e-05, + "window_size": 10, + "max_peaks": 8 + }, + 6 : { + "confidence_level": 1.57e-08, + "window_size": 10, + "max_peaks": 8 + }, + + + + }, + 'vial_insert': { + 0 : { + "confidence_level": 0.00016269120400000059, + "window_size": 9, + "max_peaks": 8 + }, + 1 : { + "confidence_level": 0.000173801014, + "window_size": 9, + "max_peaks": 8 + }, + 2 : { + "confidence_level": 0.00018948240900000074, + "window_size": 9, + "max_peaks": 8 + }, + 3 : { + "confidence_level": 0.0007136359360000004, + "window_size": 9, + "max_peaks": 8 + }, + 4 : { + "confidence_level": 0.00015286798600000018, + "window_size": 9, + "max_peaks": 8 + }, + 5 : { + "confidence_level": 0.00047967177600000013, + "window_size": 9, + "max_peaks": 8 + }, + 6 : { + "confidence_level": 0.26672138987099997, + "window_size": 9, + "max_peaks": 8 + }, + + }, + 'force_backup': { + 0 : { + "confidence_level": 0.0, + "window_size": 9, + "max_peaks": 8 + }, + 1 : { + "confidence_level": 0.00, + "window_size": 9, + "max_peaks": 8 + }, + 2 : { + "confidence_level": 0.000, + "window_size": 9, + "max_peaks": 8 + }, + 3 : { + "confidence_level": 0.00, + "window_size": 9, + "max_peaks": 8 + }, + 4 : { + "confidence_level": 0.000, + "window_size": 9, + "max_peaks": 8 + }, + 5 : { + "confidence_level": 0.00, + "window_size": 9, + "max_peaks": 8 + }, + 6 : { + "confidence_level": 0.0, + "window_size": 9, + "max_peaks": 8 + }, + } + } + + + + model_arch = "BC_RNN_GMM" + task = "pick_place" # stack_cube or pick_place + model_name = f"ensemble" + number = 'no_recovery_video' + + results_path = f"docs/training_data/{task}/uncertainty_rollout_{task}/{model_name}/run_{number}" + + uncertainties_path = f"{results_path}/uncertainties{number}.txt" + actions_path = f"{results_path}/actions{number}.txt" + min_actions_path = f"{results_path}/min_actions{number}.txt" + max_actions_path = f"{results_path}/max_actions{number}.txt" + time_taken_path = f"{results_path}/time_taken{number}.txt" + recovery_activated_during_rollout_path = f"{results_path}/recovery_activated_during_rollout.txt" + rollout_uncert_path = f"{uncertainties_path}/rollout_uncerts" + rollout_log_path = f"{uncertainties_path[:len(uncertainties_path)-4]}_rollout_log.txt" + + rollout_log_path = f"{uncertainties_path[:len(uncertainties_path)-4]}_rollout_log.txt" # remove the '.txt' and add rollout_log.txt + + uncertainty_results_path = f"{results_path}/uncertainty_plots" + trajectory_results_path = f"{results_path}/trajectory_plots" + os.makedirs(uncertainty_results_path, exist_ok=True) + os.makedirs(trajectory_results_path, exist_ok=True) + + + # clear files + clear_files( + uncertainties_path, actions_path, + min_actions_path, max_actions_path, + time_taken_path, recovery_activated_during_rollout_path + ) + + # Run policy + results = [] + failed_no_intervention =[] + intervention = [] + intervention_success = [] + knocked=0 + intervention_failure = [] + for trial in range(args_cli.num_rollouts): + print(f"[INFO] Starting trial {trial}") + + terminated, traj, recovery_activated_during_rollout, failure = rollout_ensemble(pick_place_ensemble[:args_cli.ensemble_size], env, success_term, args_cli.horizon, device, parameters['force_backup'], use_recovery=args_cli.use_recovery, rollout_num=trial) + # save the uncertainties + print("Finished rollout, recovery needed : ", recovery_activated_during_rollout) + #print("actions shape : ", traj['actions']) + + if not terminated: + print(f"failure : {traj['failure']}") + for j in range(7): + with open(f"docs/rollouts/rollout_logging_no_recovery_joint{j}.txt", "a") as file: + #file.write(f"Logs for joint {j}\n") + for i, var in enumerate(traj['actions']): + # get all the info per joint + act = str(traj['actions'][i][0][j]) + max = str(traj['max_actions'][i].values.tolist()[j]) + min = str(traj['min_actions'][i].values.tolist()[j]) + uncert = str(traj['uncertainties'][i].tolist()[j]) + #print("joint pos : ", str(traj['joint_pos'][i][0].tolist()[j])) + joint_pos = str(traj['joint_pos'][i][0].tolist()[j]) + recovery = "False"#str(traj['recovery'][0][i]) + # print("writing recovery : ", recovery) + line = f"{i} : {act} : {max} : {min} : {uncert} : {joint_pos}: {recovery}: {terminated}\n" + file.write(line) + # this logs the outcome of the rollout - the failure methods + for j in range(7): + joint_uncert = [t[j].item() for t in traj['uncertainties']] + with open(f"docs/rollouts/rollout_uncerts_joint{j}.txt", "a") as f: + line = ",".join(["{:.10f}".format(v) for v in joint_uncert]) + f.write(line + f" : {terminated} \n") + # for i in range(len(traj['failure'])): + with open(f"docs/rollouts/rollout_outcomes.txt", "a") as file: + if terminated: + # print(f"grasp : {traj['grasp']}, appr : {traj['appr']}") + #trialnum : outcome : recovery needed ? : failure mode : grasp subtask : appr subtask + line=f"{trial}: {terminated} : {recovery_activated_during_rollout} : : {True} : {True} \n" + else : + grasp = traj['grasp'][0] + appr = traj['appr'][0] + print(f"Subatask : {grasp} , {appr}") + line=f"{trial} : {terminated} : {recovery_activated_during_rollout} : {traj['failure'][0]} : {grasp} : {appr}\n" + # print(f"grasp : {traj['grasp'][0]}, appr : {traj['appr'][0]}") + file.write(line) + + + results.append(terminated) + if not terminated: + if recovery_activated_during_rollout <=0: + failed_no_intervention.append(1) + + if recovery_activated_during_rollout > 0: + intervention.append(1) + if terminated: + intervention_success.append(1) + else: + intervention_failure.append(1) + else: + intervention.append(0) + if "knocked" in traj['failure'] or (len(traj['failure']) > 0 and traj['failure'][-1] == "knocked"): + knocked+=1 + + print(f'Current success rate: {results.count(True)/len(results):.2%}') + print(f'Current intervention rate: {sum(intervention)/len(intervention):.2%} ({sum(intervention)}/{len(intervention)})') + print(f"[INFO] Trial {trial}: {'SUCCESS' if terminated else 'FAILED'}\n") + #loghelper.stopEpoch(trial) + + # Prepare summary text + exp_name = args_cli.exp_name if args_cli.exp_name else "unnamed" + summary_text = f"\nSummary for experiment {exp_name}\n" + summary_text += f"\nSuccessful trials: {results.count(True)}, out of {len(results)} trials\n" + summary_text += f"Success rate: {results.count(True) / len(results)}\n" + summary_text += f'Failed runs with no intervention : {sum(failed_no_intervention)}\n' + summary_text += f"Intervention rate : {sum(intervention)/len(intervention)}. Of which success : {sum(intervention_success)}, of which failed {sum(intervention_failure)}\n" + summary_text += f"Intervention Results: {intervention}\n" + summary_text += f"Number of times object knocked over : {knocked}\n" + summary_text += f"Trial Results: {results}\n\n" + summary_text += f"Environment random seed: {args_cli.seed}\n" + + # Print to console + print(summary_text) + + # Write to file + results_file = "docs/ensemble_run_results.txt" + with open(results_file, 'a') as f: + f.write(summary_text) + f.write("\n" + "="*80 + "\n\n") + print(f"Results appended to: {results_file}") + + if franka_gripper is not None: + franka_gripper.destroy_node() + if franka_arm is not None: + franka_arm.destroy_node() + if rclpy.ok(): + rclpy.shutdown() + env.close() + + + +if __name__ == "__main__": + # run the main function + main() + # close sim app + simulation_app.close() diff --git a/scripts/imitation_learning/robomimic/play_ensemble_v05.py b/scripts/imitation_learning/robomimic/play_ensemble_v05.py index e3897f85f443..8193f526fc12 100644 --- a/scripts/imitation_learning/robomimic/play_ensemble_v05.py +++ b/scripts/imitation_learning/robomimic/play_ensemble_v05.py @@ -100,6 +100,12 @@ from isaaclab.envs.mdp.actions.binary_joint_actions import BinaryJointPositionAction from backup_controller_handler import BackupController +# --- ROS2 imports --- +# --- UDP Bridge imports --- +import socket +import json +import time +import numpy as np def policy_from_checkpoint_v05(ckpt_path, device, verbose=True): """ @@ -175,96 +181,6 @@ def policy_from_checkpoint_v05(ckpt_path, device, verbose=True): return FileUtils.policy_from_checkpoint(ckpt_path=ckpt_path, device=device, verbose=verbose) -def rollout(policy, env, success_term, horizon, device): - """Perform a single rollout of the policy in the environment. - - Args: - policy: The robomimicpolicy to play. - env: The environment to play in. - horizon: The step horizon of each rollout. - device: The device to run the policy on. - - Returns: - terminated: Whether the rollout terminated. - traj: The trajectory of the rollout. - """ - policy.start_episode() - obs_dict, _ = env.reset() - - #print(f"obs_dict type: {type(obs_dict)},\nobs_dict contents:\n {obs_dict}") - - traj = dict(actions=[], obs=[], next_obs=[], sub_obs=[], uncertainties=[]) - - # Define the observation keys the trained model expects - TRAINED_OBS_KEYS = ['eef_pos', 'eef_quat', 'gripper_pos', 'joint_pos', 'joint_vel', - 'object_position', 'target_object_position', 'actions'] - - for i in range(horizon): - # Prepare observations - obs = copy.deepcopy(obs_dict["policy"]) - sub_obs = copy.deepcopy(obs_dict["subtask_terms"]) - - for ob in obs: - obs[ob] = torch.squeeze(obs[ob]) - #print(f"found observation : {obs[ob]}") - - for subob in sub_obs: - sub_obs[subob] = torch.squeeze(sub_obs[subob]) - - # Check if environment image observations - if hasattr(env.cfg, "image_obs_list"): - # Process image observations for robomimic inference - for image_name in env.cfg.image_obs_list: - if image_name in obs_dict["policy"].keys(): - # Convert from chw uint8 to hwc normalized float - image = torch.squeeze(obs_dict["policy"][image_name]) - image = image.permute(2, 0, 1).clone().float() - image = image / 255.0 - image = image.clip(0.0, 1.0) - obs[image_name] = image - - traj["obs"].append(obs) - traj["sub_obs"].append(sub_obs) - - # Filter observations to only include keys the model was trained with - policy_obs = {k: obs[k] for k in TRAINED_OBS_KEYS if k in obs} - - # Add dropout layers to the model and calculate uncertainty and remove at the end to not effect final action - - #hooks = inject_dropout_layers(policy=policy, probability=0.1) - # uncertainty_dict = MC_dropout_uncertainty(policy=policy, obs=obs, niters=15) - # traj['uncertainties'].append(uncertainty_dict['variance']) - #remove_dropout_layers(hooks) - - # Compute actions - actions = policy(policy_obs) - - # Unnormalize actions - if args_cli.norm_factor_min is not None and args_cli.norm_factor_max is not None: - actions = ( - (actions + 1) * (args_cli.norm_factor_max - args_cli.norm_factor_min) - ) / 2 + args_cli.norm_factor_min - - actions = torch.from_numpy(actions).to(device=device).view(1, env.action_space.shape[1]) - - # Apply actions - obs_dict, _, terminated, truncated, _ = env.step(actions) - obs = obs_dict["policy"] - sub_obs = obs_dict["subtask_terms"] - - # Record trajectory - traj["actions"].append(actions.tolist()) - traj["next_obs"].append(obs) - - - # Check if rollout was successful - if bool(success_term.func(env, **success_term.params)[0]): - return True, traj - elif terminated or truncated: - return False, traj - - return False, traj - # tip over detection def has_fallen(asset, threshold: float = -0.7, debug: bool = True) -> bool: @@ -303,8 +219,7 @@ def has_fallen(asset, threshold: float = -0.7, debug: bool = True) -> bool: return uprightness > threshold - -def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, use_recovery=True, rollout_num=0): +def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, use_recovery=True, rollout_num=0): """Perform a single rollout of the policy in the environment. Args: @@ -319,9 +234,13 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, """ ## do setup print("running rollout number : ", rollout_num) + # Initialize UDP Bridge Client + + for policy in ensemble: policy.start_episode() obs_dict, _ = env.reset() + #print("obs_dict keys : ", obs_dict.keys()) traj = dict(actions=[], obs=[], next_obs=[], sub_obs=[], uncertainties=[], min_actions=[], max_actions=[], time_taken=[], joint_pos=[], recovery=[], failure=[], grasp=[], appr=[], diff=[]) @@ -332,6 +251,7 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, ###### SET UP RECOVERY #### use_recovery=False + recovery_activated_during_rollout = 0 print("rollout recovery enabled ? : ", use_recovery) @@ -344,21 +264,29 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, max_recovery_steps = 500 # Safety limit for recovery mode ##### CONFIG RECOVERY CONTROLLER #### - backup_controller = BackupController(env, device, tasktype="place") + backup_controller = BackupController(env, device, tasktype="lift") state_guess = 0 last_state = 0 recovery_mode = False object_knocked = False + prev_actions = None # hold previous action + prev_gripper_action = None # Define the observation keys the trained model expects # These must match the keys used during training (from shape_metadata) TRAINED_OBS_KEYS = ['eef_pos', 'eef_quat', 'gripper_pos', 'joint_pos', 'joint_vel', 'object_position', 'target_object_position', 'actions'] print(f'Running for horizon {horizon}') + summary_text = f"\nSetting for {rollout_num} \n" + summary_text += f"Stirplate : {env.unwrapped.scene['stirplate'].data.root_pose_w} \ngoal : {env.unwrapped.command_manager.get_command('object_pose')}" + # Write to file + + + for i in range(horizon): # Prepare observations - + #print("obs_dict keys : ", obs_dict.keys()) obs = copy.deepcopy(obs_dict["policy"]) sub_obs = copy.deepcopy(obs_dict["subtask_terms"]) @@ -381,7 +309,9 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, image = image / 255.0 image = image.clip(0.0, 1.0) obs[image_name] = image - + # eef_pos is (num_envs, 3), eef_quat is (num_envs, 4) + + # Filter observations to only include keys the model was trained with # This prevents dimension mismatch errors when env has extra observations policy_obs = {k: obs[k] for k in TRAINED_OBS_KEYS if k in obs} @@ -458,14 +388,17 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, ) / 2 + args_cli.norm_factor_min actions = actions.to(device=device).view(1, env.action_space.shape[1]) - + # Apply actions obs_dict, _, terminated, truncated, _ = env.step(actions) recovery.append(recovery_mode) # update obs from taking action obs = obs_dict["policy"] sub_obs = obs_dict["subtask_terms"] + + # Compute absolute pose for the ROS bridge + # Record trajectory traj["actions"].append(actions.tolist()) traj["next_obs"].append(obs) @@ -490,6 +423,13 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, traj['appr'].append(appr.item()) print(f"grasp : {traj['grasp']}, appr : {traj['appr']}") # print(f"grasp : {grasp.item()}, appr : {appr.item()}") + summary_text += f"\nRecovery : {recovery_activated_during_rollout}" + summary_text += f"Result : success \n \n " + setting_file = "docs/ensemble_run_setup.txt" + with open(setting_file, 'a') as f: + f.write(summary_text) + f.write("\n" + "="*80 + "\n\n") + print(f"Results appended to: {setting_file}") return True, traj, recovery_activated_during_rollout, "" elif terminated or truncated: @@ -503,12 +443,31 @@ def rollout_ensemble(ensemble, env, success_term, horizon, device, parameters, #if the failure was caused by object collision traj['failure'].append("timeout") failure_reason = "timeout" + summary_text += f"\nRecovery : {recovery_activated_during_rollout}" + summary_text += f"Result : timeout \n \n " else: #if failure due to timeout, record timeout traj['failure'].append("knocked") failure_reason = "object_knocked" + summary_text += f"\nRecovery : {recovery_activated_during_rollout}" + summary_text += f"Result : hit\n \n " + + setting_file = "docs/ensemble_run_setup.txt" + with open(setting_file, 'a') as f: + f.write(summary_text) + f.write("\n" + "="*80 + "\n\n") + print(f"Results appended to: {setting_file}") return False, traj, recovery_activated_during_rollout, failure_reason + prev_actions = actions #print("REcovery : ", traj["recovery"]) + + summary_text += f"\nRecovery : {recovery_activated_during_rollout}" + summary_text += f"Result : failed, timeout \n \n " + setting_file = "docs/ensemble_run_setup.txt" + with open(setting_file, 'a') as f: + f.write(summary_text) + f.write("\n" + "="*80 + "\n\n") + print(f"Results appended to: {setting_file}") traj['failure'].append("timeout") grasp = torch.any(sub_obs['grasp']) traj['grasp'].append(grasp.item()) @@ -549,7 +508,7 @@ def clear_files(*paths : str): case _: exit() - + def main(): """Run a trained policy from robomimic with Isaac Lab environment.""" @@ -593,12 +552,12 @@ def main(): # stack_cube_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/stack_cube_ensemble.txt') #pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/ensembles.txt') - pick_place_ensemble = load_ensemble(device, ensemble_path='docs/place/low/Dev-IK-Rel-Place-v0/best_models/best_model_paths.txt') + #pick_place_ensemble = load_ensemble(device, ensemble_path='docs/place/low/Dev-IK-Rel-Place-v0/best_models/best_model_paths.txt') # pick_place_ensemble_30 = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/pick_place_ensemble_30_paths.txt') - # pick_place_ensemble = load_ensemble(device, ensemble_path='docs/lift/Dev-IK-Rel-v1/best_models/best_model_paths.txt') + pick_place_ensemble = load_ensemble(device, ensemble_path='docs/lift/Dev-IK-Rel-v1/best_models/best_model_paths.txt') # pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/insert_paths.txt') - #pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/inser_top_paths.txt') + # pick_place_ensemble = load_ensemble(device, ensemble_path='scripts/imitation_learning/robomimic/inser_top_paths.txt') # Lets set these to the 0.99 confidence parameters = { @@ -678,8 +637,6 @@ def main(): "window_size": 9, "max_peaks": 8 }, - - } } @@ -726,7 +683,7 @@ def main(): for trial in range(args_cli.num_rollouts): print(f"[INFO] Starting trial {trial}") - terminated, traj, recovery_activated_during_rollout, failure = rollout_ensemble(pick_place_ensemble[:args_cli.ensemble_size], env, success_term, args_cli.horizon, device, parameters['beaker_lift'], use_recovery=args_cli.use_recovery, rollout_num=trial) + terminated, traj, recovery_activated_during_rollout, failure = rollout_ensemble(pick_place_ensemble[:args_cli.ensemble_size], env, success_term, args_cli.horizon, device, parameters['vial_insert'], use_recovery=args_cli.use_recovery, rollout_num=trial) # save the uncertainties print("Finished rollout, recovery needed : ", recovery_activated_during_rollout) #print("actions shape : ", traj['actions']) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_obs.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_obs.py index c3fea274b1a4..3ec3160a7915 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_obs.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_env_obs.py @@ -30,9 +30,11 @@ def __post_init__(self): # put the beaker on the stir plate glassware = ChemistryGlassware() self.scene.stirplate = glassware.stirplate(pos=[0.5, 0.0, 0.01]) + self.scene.goal_plate_pos = glassware.scale(pos=[0.4, -0.3, 0.01]) self.events.reset_object_position = EventTerm( func=mdp.reset_multiple_root_state_uniform, mode="reset", + #[0.54, -0.3, 0.0] params={ "pose_range": {"x": (0, 0.2), "y": (0, 0.25), "z": (0.02, 0.02)}, "velocity_range": {}, diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_vial_insert_top_down.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_vial_insert_top_down.py index 6b1ba76ef207..a58caa636834 100644 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_vial_insert_top_down.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/config/franka/dev_ik_rel_vial_insert_top_down.py @@ -106,7 +106,7 @@ def __post_init__(self): ) self.terminations.success= DoneTerm(func=mdp.object_inserted_upright, params={"lower_object_cfg": SceneEntityCfg("vialrack"), "upright_good_deg": 22.5}) - + self.terminations.object_tipped = DoneTerm(func=mdp.object_knocked, params={"max_tilt": 65}) #self.terminations.success=DoneTerm(func=mdp.object_stacked_upright, params={"lower_object_cfg": SceneEntityCfg("scale")}) self.observations.subtask_terms.appr_goal=ObsTerm(func=mdp.is_object_lifted, params={"threshold":0.15} diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py index 13295c35c41b..5bb1883807ee 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py @@ -98,7 +98,7 @@ class CommandsCfg: debug_vis=True, ranges=mdp.UniformPoseCommandCfg.Ranges( # pos_x=(0.3,0.3), pos_y=(-0.28, -0.28), pos_z=(0.1, 0.1), roll=(0.0, 0.0), pitch=(0.0, 0.0), yaw=(0.0, 0.0) - pos_x=(0.4,0.4), pos_y=(-0.3, -0.3), pos_z=(0.08, 0.08), roll=(math.pi/2, math.pi/2), pitch=(math.pi/2,math.pi/2), yaw=(0.0, 0.0) + pos_x=(0.4,0.4), pos_y=(-0.3, -0.3), pos_z=(0.11, 0.11), roll=(math.pi/2, math.pi/2), pitch=(math.pi/2,math.pi/2), yaw=(0.0, 0.0) ), ) @@ -356,8 +356,8 @@ def __post_init__(self): # simulation settings self.sim.dt = 0.01 # 100Hz self.sim.render_interval = self.decimation - self.viewer.eye = (1.0, 1.0, 1.0) - self.viewer.lookat = (0.0, 0.0, -0.1) + self.viewer.eye = (2.5, 0.5, 0.5) + self.viewer.lookat = (0.0, 0.0, 0.0) # self.terminations.set_loghelper(self.loghelper) #self.events.set_loghelper(self.loghelper) # self.observations.policy.set_loghelper(self.loghelper) From 526d0e761e2ec597139b73403032d0ce5e518f6a Mon Sep 17 00:00:00 2001 From: LJones-alt Date: Tue, 17 Mar 2026 16:43:05 +0000 Subject: [PATCH 15/15] case ready --- ...itching_for_Robust_Robotic_Chemistry-1.pdf | Bin 0 -> 1267794 bytes .../teleoperation/teleop_se3_agent.py | 80 +++++++++--------- 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z4$dIbhpx1-Xlpww7t$?){I*VkB=3VvK@gA>shEKz_3XURAh--jQtc+lR32&xvqsB+ zrC_p_C>azKE^7&aqTnzY7y@QxX=8;#%A?_Mgp@1}HN;g5+ui!g|0<1To8;v0T8<~6V7h7k^q=k73s_8o<0B_!o z)jo#Z$Xvf&ykRk1vW%Mm81j!&@+~X{jAtBCG#a*0ooVj(-aEQT@VV None: teleop_interface.reset() prev_gripper_action = None - # Sync real robot with simulation initial pose - if bridge is not None: - # Get simulated robot initial EE pose - try: - # We use the observation manager to get the tracked end-effector pose - # Based on your environment info, these keys are available in the 'policy' group - obs_dict = env.observation_manager.compute_group("policy") + # # Sync real robot with simulation initial pose + # if bridge is not None: + # # Get simulated robot initial EE pose + # try: + # # We use the observation manager to get the tracked end-effector pose + # # Based on your environment info, these keys are available in the 'policy' group + # obs_dict = env.observation_manager.compute_group("policy") - # eef_pos is (num_envs, 3), eef_quat is (num_envs, 4) - eef_pos = obs_dict["eef_pos"][0].cpu().numpy() - eef_quat = obs_dict["eef_quat"][0].cpu().numpy() # [qw, qx, qy, qz] + # # eef_pos is (num_envs, 3), eef_quat is (num_envs, 4) + # eef_pos = obs_dict["eef_pos"][0].cpu().numpy() + # eef_quat = obs_dict["eef_quat"][0].cpu().numpy() # [qw, qx, qy, qz] - # ROS expects [qx, qy, qz, qw], Isaac uses [qw, qx, qy, qz] - sim_init_pose = [ - float(eef_pos[0]), float(eef_pos[1]), float(eef_pos[2]), - float(eef_quat[1]), float(eef_quat[2]), float(eef_quat[3]), float(eef_quat[0]) - ] - sim_pose = [0.5208, 0.0096, 0.3751, 0.0360, 0.7414, -0.0706, 0.6663] - print(f"Simulated robot initial pose: {sim_init_pose}") - # bridge.init_real(sim_init_pose) - except Exception as e: - print(f"[WARN] Could not retrieve sim initial pose from observations: {e}") - print("[INFO] Skipping real robot synchronization.") + # # ROS expects [qx, qy, qz, qw], Isaac uses [qw, qx, qy, qz] + # sim_init_pose = [ + # float(eef_pos[0]), float(eef_pos[1]), float(eef_pos[2]), + # float(eef_quat[1]), float(eef_quat[2]), float(eef_quat[3]), float(eef_quat[0]) + # ] + # sim_pose = [0.5208, 0.0096, 0.3751, 0.0360, 0.7414, -0.0706, 0.6663] + # print(f"Simulated robot initial pose: {sim_init_pose}") + # # bridge.init_real(sim_init_pose) + # except Exception as e: + # print(f"[WARN] Could not retrieve sim initial pose from observations: {e}") + # print("[INFO] Skipping real robot synchronization.") print("Teleoperation started. Press 'R' to reset the environment.") @@ -351,7 +351,7 @@ def stop_teleoperation() -> None: actions = action.repeat(env.num_envs, 1).to(env.device) # Compute absolute pose for the ROS bridge - with torch.no_grad(): + """ with torch.no_grad(): # Get latest observations from the 'policy' group obs_dict = env.observation_manager.compute_group("policy") eef_pos = obs_dict["eef_pos"] # (num_envs, 3) @@ -369,25 +369,25 @@ def stop_teleoperation() -> None: abs_pose_bridge = torch.cat([abs_pos, abs_quat_ros], dim=-1) print(f"absolute pose: {eef_pos}, {eef_quat}") print(f"absolute pose action: {abs_pose_bridge}") - # ROS 2 Synchronization via Bridge - if bridge is not None: - # Send absolute pose to bridge - bridge.publish_command(abs_pose_bridge[0]) + # # ROS 2 Synchronization via Bridge """ + # if bridge is not None: + # # Send absolute pose to bridge + # bridge.publish_command(abs_pose_bridge[0]) - # Send joint positions for MoveIt 2 - obs_dict_full = env.observation_manager.compute_group("policy") - if "joint_pos" in obs_dict_full: - bridge.publish_joints(obs_dict_full["joint_pos"][0]) - - # Handle gripper toggling - curr_gripper = float(actions[0, -1]) - if prev_gripper_action is not None: - if curr_gripper != prev_gripper_action: - if curr_gripper > 0: - bridge.open_gripper() - else: - bridge.close_gripper() - prev_gripper_action = curr_gripper + # # Send joint positions for MoveIt 2 + # obs_dict_full = env.observation_manager.compute_group("policy") + # if "joint_pos" in obs_dict_full: + # bridge.publish_joints(obs_dict_full["joint_pos"][0]) + + # # Handle gripper toggling + # curr_gripper = float(actions[0, -1]) + # if prev_gripper_action is not None: + # if curr_gripper != prev_gripper_action: + # if curr_gripper > 0: + # bridge.open_gripper() + # else: + # bridge.close_gripper() + # prev_gripper_action = curr_gripper # apply actions (relative as intended by the environment) env.step(actions) diff --git a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py index 5bb1883807ee..afdc2d1174fa 100755 --- a/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py +++ b/source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/cube_lift/lift_env_cfg.py @@ -294,7 +294,7 @@ class TerminationsCfg: time_out = DoneTerm(func=mdp.time_out, time_out=True) - #joint_violation = DoneTerm(func=mdp.joint_pos_out_of_limit) + joint_violation = DoneTerm(func=mdp.joint_pos_out_of_limit) #object_orientation = DoneTerm(func=mdp.bad_orientation) @@ -356,8 +356,8 @@ def __post_init__(self): # simulation settings self.sim.dt = 0.01 # 100Hz self.sim.render_interval = self.decimation - self.viewer.eye = (2.5, 0.5, 0.5) - self.viewer.lookat = (0.0, 0.0, 0.0) + self.viewer.eye = (2.5, 0.5, 0.9) + self.viewer.lookat = (0.0, 0.0, 0.2) # self.terminations.set_loghelper(self.loghelper) #self.events.set_loghelper(self.loghelper) # self.observations.policy.set_loghelper(self.loghelper)

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