Loco-manipulation using RL and Pretrained AMO Controller in MuJoCo
Combining reinforcement learning with a pretrained AMO (Any Motion) controller for loco-manipulation tasks in MuJoCo physics simulator.
This project explores long-horizon loco-manipulation for humanoids by building a hybrid control framework that integrates locomotion controllers with RL-based manipulation policies.
Overview
I am building a hybrid loco-manipulation control framework by integrating a locomotion controller with RL-based PPO-trained manipulation policies, enabling long-horizon factory tasks in MuJoCo with reward design. Additionally, I am training lightweight LLMs for SOP-driven high-level retrieval and planning to generate multi-step task plans and sequence MuJoCo skills with pre/post-condition checks.
Technical Approach
- Simulation Environment: MuJoCo
- Base Controller: Locomotion controller integrated with RL policies
- Learning Method: Proximal Policy Optimization (PPO)
- Task: Long-horizon loco-manipulation for humanoids
- Additional Components: Lightweight LLMs for high-level planning and task sequencing
Robot performing loco-manipulation task in MuJoCo.