Robot Learning
01
A research-oriented project connecting simulation, learning-based control, and real robot deployment thinking.
Project Detail
Reinforcement Learning Based Locomotion Control for an Omnidirectional Wheeled-Bipedal Robot
2026.01 - PresentProf. Hua Chen, ZJUI
Keywords
Reinforcement LearningIsaacLabMuJoCoPPOSim-to-Real
Overview
This project focuses on learning-based locomotion control for an omnidirectional wheeled-bipedal robot operating on complex terrain. The work connects simulator setup, policy training, controller understanding, and deployment validation.
My Contributions
- Studied the code structure, training workflow, and control framework of reinforcement learning locomotion projects.
- Configured simulation environments with IsaacLab and MuJoCo Playground.
- Reproduced PPO-based locomotion policies and organized the training pipeline.
- Completed a workflow covering simulation training, sim-to-sim validation, and sim-to-real deployment preparation.
- Analyzed robot models, observation spaces, reward design, and deployment results to understand terrain adaptation mechanisms.
Outcomes
- Built a clearer understanding of learning-based robot motion control pipelines.
- Connected reinforcement learning policy training with practical deployment considerations.
- Prepared this project as the main research highlight for future graduate research applications.
Project Signal
A research-oriented project connecting simulation, learning-based control, and real robot deployment thinking.