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  • University of California, Berkeley
  • Seoul - Berkeley - Manhattan
  • 02:24 (UTC -07:00)

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mintaeshkim/README.md

Mintae Kim

Current State

  • Ph.D. Student in Mechanical Engineering at Hybrid Robotics Lab, BAIR, UC Berkeley
  • Working on Reinforcement Learning, World Models, and Reasoning

Research Interests

High-Level

Decision-Centric World Models, Safe and Robust RL, Scalable RL

  • What is intelligence?
  • How can agents achieve safety, robustness, stability, efficiency, and continual learning?
  • Can agents performance a.s. monotone increase using any data stream? (= Is RL scalable?)

Low-Level (Continually updated)

World Models, Offline RL, Off2On RL, Off-Policy RL, Stochastic Control, Reasoning

Pinned Loading

  1. roverfly roverfly Public

    🛸 RoVerFly: Robust and Versatile Implicit Hybrid Control of Quadrotor-Payload Systems

    Python 16 2

  2. flappy_control flappy_control Public

    🦇 Bat-Inspired Flapping Wing UAV Control (IROS 2025)

    Python 5