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ahmedsohail2003/README.md

Ahmed Sohail Butt

Computer Science student at Wilfrid Laurier University building practical systems across robotics & embodied AI, data engineering, cloud infrastructure, and payments.

I enjoy the part of software delivery where a loosely defined problem becomes clear requirements, measurable success criteria, and working software.

🌐 Portfolio: ahmedsohail2003.github.io — all projects by discipline, with the five-part WorkCell series at /workcell.

🤖 Physical AI: one simulated industrial work-cell, five learning approaches

One robot (SO-ARM100 in MuJoCo), one manipulation task, taught five different ways — with honest measurement throughout. Every project below shares the same simulated work-cell, publishes its datasets/models, and reports failures as carefully as successes.

WorkCell series Approach Headline result
Part A · Sim2Cell Imitation learning (ACT) + domain randomization 90% success, 87–100% robust under randomization — incl. finding & correcting a false "0% collapse" caused by an eval-harness rendering bug + a claim-by-claim ACT-paper reproduction report
Part B · TalkToTheCell Vision-language-action (SmolVLA 450M fine-tune, free-tier GPU) 0% → 90%, commanded in natural language; the same dataset moved ACT −15 and SmolVLA +35 — a two-sided architecture-vs-data study
Part C · GraspSight Classical perception: RGB-D → point cloud → 6-DoF pose → grasp 20/20 end-to-end picks, 0.58 mm ICP pose error, 0.32 mm hand-eye calibration
Part D · PolicyForge Reinforcement learning + from-scratch world model World-model planner solves Reach in 8k env steps (37× fewer than PPO); contact-rich Lift solved via a 4-stage reward-design study
Part E · CellOps ROS 2: SLAM + Nav2, C++ grasp service 12/12 Nav2 goals on a self-built SLAM map; Part C's estimator ported to C++ at 0.000 mm parity, 3.3× faster, served over ROS 2
Datasets & models 4 published LeRobot datasets + 5 model cards on 🤗 Every result above is reproducible from public artifacts

Upstream open source: root-caused and fixed an unsafe video-backend selection bug in LeRobot, merged upstream as a co-authored commit after maintainer review (#4307, superseding my #4177); a second fix for Windows Hub path normalization is under review (#4174), plus independent Windows verification of #4121.

Featured work

Business Intelligence Platform

An end-to-end data product for a residential painting business:

  • 22 functional requirements, user flows, and measurable acceptance criteria
  • Python ETL with pandas, SQLAlchemy, watchdog, and automated tests
  • Azure SQL provisioned through Bicep and Azure CLI
  • Typed SQL reporting views and a .NET Blazor/Dapper analytics dashboard
  • Privacy-safe, fixed-seed synthetic demo data

Multi-Agent Adversarial AI System

A four-person academic project comparing symbolic and generative AI agents:

  • Simple, Minimax, Alpha-Beta, and Gemini-powered players
  • My ownership: Gemini prompt construction, response validation, corrective re-prompting, and evaluation
  • Metrics for search nodes, real API requests, first-try validity, and retries
  • Python test suite and GitHub Actions CI

Transfer Platform

An educational e-Transfer-style system built as two Spring Boot microservices:

  • REST-based account and transfer services with separate data ownership
  • Account, amount, currency, and insufficient-funds validation
  • Saga-style compensation when a credit fails after a debit
  • 45 JUnit, Mockito, and MockMvc tests
  • OpenAPI, Docker Compose, Kubernetes, and CI

Tools I work with

Languages: Python, SQL, Java, C#/.NET, JavaScript, C/C++

Robotics & ML: PyTorch, LeRobot, Hugging Face, MuJoCo, RL (PPO/SAC), world models, VLA fine-tuning (SmolVLA), 6-DoF grasping, sim-to-real methodology

AI and data: Google GenAI SDK, prompt engineering, pandas, NumPy, SQLAlchemy, ETL pipelines, statistical evaluation

Cloud and backend: Azure SQL, Bicep, Azure CLI, Spring Boot, REST APIs, Dapper, Docker, Kubernetes

Delivery: Requirements analysis, user flows, acceptance criteria, technical documentation, relational modelling, Git/GitHub

Currently focused on

  • Building AI prototypes with observable quality and explicit fallback behaviour
  • Designing trustworthy data workflows from raw ingestion through decision-ready reporting
  • Learning distributed-systems and payments concepts through testable projects

Contact

Popular repositories Loading

  1. Multi-Agent-Adversarial-Game Multi-Agent-Adversarial-Game Public

    Benchmarks Minimax and Alpha-Beta agents against a Google Gemini agent with validation, corrective re-prompting, and evaluation metrics.

    Python 1

  2. workcell-partC-grasping workcell-partC-grasping Public

    From-scratch RGB-D perception-to-grasp pipeline in MuJoCo: segmentation -> point cloud -> 6-DoF pose (yaw-sweep + trimmed ICP, 0.58mm) -> grasp -> closed-loop pick (20/20)

    Python 1

  3. YPYW-Business-Intelligence YPYW-Business-Intelligence Public

    Full-stack BI platform with Python ETL, Azure SQL IaC, analytics views, and a .NET Blazor dashboard built from documented business requirements.

    HTML

  4. transfer-platform transfer-platform Public

    Educational e-Transfer-style platform using Spring Boot microservices, REST orchestration, compensation logic, Docker, Kubernetes, and 45 tests.

    Java

  5. ahmedsohail2003 ahmedsohail2003 Public

    GitHub profile README for Ahmed Sohail Butt — AI, data, cloud, and payments projects.

  6. workcell-partA-imitation workcell-partA-imitation Public

    End-to-end imitation learning on a MuJoCo SO-ARM100 work-cell: scripted expert -> LeRobotDataset -> ACT -> domain-randomization robustness study (90% nominal, 87-100% under DR)

    Python