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.
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.
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
Self-paced learning and playground for robotics control software architecture and design patterns
Sim2real transfer of a RL policy using brax and mujoco
Sparse, Residual World Models for Object-Centric Manipulation — code, paper, and browser demo
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