Reinforcement learning with Unitree G1 humanoid - Dev w/ G1 P.5
sentdex
Jul 25, 2025
Developing robust robotic arm control requires more than just raw compute; it demands careful constraints on joint limits and reward shaping to prevent hardware failure. By utilizing Proximal Policy Optimization (PPO), the author demonstrates that a tiny 2x64 model can effectively map Cartesian coordinates to precise physical movements, despite the persistent risks of self-collision and system overload.
Key insight: The author achieves high-speed arm control with a model file of only 184 kilobytes, proving that extremely lightweight neural networks are sufficient for complex robotic navigation if the reward function is well-structured.
