han-humanoid-motion-control-v1

Overview

han-humanoid-motion-control-v1 is a reinforcement learning model designed for humanoid robot locomotion and balance control in simulated environments.

Model Type

Reinforcement Learning (PPO)

Framework

PyTorch

Training Environment

• MuJoCo Simulation
• Custom Humanoid Gym Environment
• 10M training steps

Objective

Optimize walking stability, obstacle avoidance, and directional control for humanoid robots.

Performance

• Stable walking success rate: 93%
• Fall reduction improvement: +27% over baseline
• Convergence achieved after ~8M steps

Use Cases

• Humanoid robotics research
• Motion planning simulation
• Robotics AI experimentation

Limitations

• Trained only in simulation
• Not yet validated on real hardware

License

MIT License