Introduction

The spinal cord in many vertebrates contains a central pattern generator (CPG)[1] that can control the physical body to interact with the environment and produce diverse rhythmic motor patterns, such as walking and swimming. And amphibious robots are good biological counterparts of animals for understanding their locomotion. In this project, we would like to use the Deep reinforcement learning framework BRAX[2] in MuJoCo[3] for exploring and designing CPG controllers for a bio-inspired robot Polymander[4] and then verify the controller in real experiments.

Main Contents (PdM contents in bold)

• Migrate the existing CPG-controller into MuJoCo Jax framework to prepare robot simulation experiments
• Use Deep reinforcement learning to train a CPG network for Polymander to walk [, swim, and transition] in different terrains. If needed, access to EPFL scitas computing cluster would be provided.
• Deploy the network on real robots for validation [and reduce the reality gap].

The student is expected to be rigorous and patient, have good programming skills and prior knowledge in reinforcement learning. Having taken the course CS-433 Computational Motor Control is a plus, but not mandatory. Students interested in this project could send CV, transcripts, and materials that can demonstrate project experience, if possible, to supervisors listed above.

Reference

• Ijspeert, Auke Jan, et al. "From swimming to walking with a salamander robot driven by a spinal cord model." science 315.5817 (2007): 1416-1420.

• Freeman, C. Daniel, et al. "Brax--a differentiable physics engine for large scale rigid body simulation." arXiv preprint arXiv:2106.13281 (2021).
• Todorov, Emanuel, Tom Erez, and Yuval Tassa. "Mujoco: A physics engine for model-based control." 2012 IEEE/RSJ international conference on intelligent robots and systems. IEEE, 2012.
• Gevers, Louis, et al. "Investigating the effect of morphology on the terrestrial gaits of amphibious fish using a reconfigurable robot." Bioinspiration & Biomimetics (2025).