Learning Skateboarding for Humanoid Robots through Massively Parallel Reinforcement Learning

2024/09/12 by William Thibault, Vidyasagar Rajendran, Thibault, William +5 · 1 citation
Engineering · #FOS: Computer and information sciences #Robotic Locomotion and Control #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2409.07846

openalex publication_date 2024/09/12 · openalex created_date 2024/10/23 · openalex updated_date 2026/07/28

Abstract

Learning-based methods have proven useful at generating complex motions for robots, including humanoids. Reinforcement learning (RL) has been used to learn locomotion policies, some of which leverage a periodic reward formulation. This work extends the periodic reward formulation of locomotion to skateboarding for the REEM-C robot. Brax/MJX is used to implement the RL problem to achieve fast training. Initial results in simulation are presented with hardware experiments in progress.

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