Humanoid Loco-Manipulation Policy Learning
RL-based whole-body control for locomotion, reaching, and motion tracking.
This project focuses on scalable policy learning for humanoid robots that must walk, manipulate, and track motion under realistic dynamics.
Key contributions:
- Built RL pipelines for robust whole-body control across locomotion and object-reaching tasks.
- Improved policy quality and adaptability using teacher-student distillation from retargeted human demonstrations.
- Designed adaptive behaviors such as low-object reaching with natural full-body motion.
- Prioritized sim-to-real robustness to transfer policies from simulation to physical humanoid platforms.
Technical stack:
- IsaacLab / IsaacGym
- PyTorch
- ROS