An example using an LLM to navigate a quadruped in an unknown environment through trial and error. Based on mujoco playground.
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Updated
Apr 16, 2025 - Python
An example using an LLM to navigate a quadruped in an unknown environment through trial and error. Based on mujoco playground.
Repository created using https://github.com/google-deepmind/mujoco_playground as base in order to add the Go2 walking and handstand policies. It contains the code to train and simulate the policy.
Run JAX and MuJoCo MJX on AMD Ryzen AI MAX (Strix Halo, gfx1151) from pip wheels - no system ROCm - with a hang-safe check that tells you which wheel set works
A small humanoid robot you can talk to: walks with a reinforcement-learned policy, waves and dances, shows a face with lip sync and speaks - its brain is a local LLM you run yourself (Ollama or any OpenAI-compatible API). MuJoCo simulation, sim-to-real.
Sparse, Residual World Models for Object-Centric Manipulation — code, paper, and browser demo
Sim2real transfer of a RL policy using brax and mujoco
Self-paced learning and playground for robotics control software architecture and design patterns
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