Learning Velocity-based Humanoid Locomotion: Massively Parallel Learning with Brax and MJX

TL;DR

This paper introduces a velocity-based reinforcement learning approach for humanoid locomotion, utilizing parallel simulation in Brax and MJX to enable fast training and potential real-world application.

Contribution

It presents a novel velocity-based RL policy for humanoid robots, implemented in Brax/MJX for efficient parallel training and future real-world deployment.

Findings

Fast training achieved with Brax/MJX simulation

Policy demonstrates effective velocity-based locomotion

Simulation results show promising performance

Abstract

Humanoid locomotion is a key skill to bring humanoids out of the lab and into the real-world. Many motion generation methods for locomotion have been proposed including reinforcement learning (RL). RL locomotion policies offer great versatility and generalizability along with the ability to experience new knowledge to improve over time. This work presents a velocity-based RL locomotion policy for the REEM-C robot. The policy uses a periodic reward formulation and is implemented in Brax/MJX for fast training. Simulation results for the policy are demonstrated with future experimental results in progress.