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
