Learning Skateboarding for Humanoid Robots through Massively Parallel Reinforcement Learning

William Thibault; Vidyasagar Rajendran; William Melek; Katja Mombaur

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arXiv · 2409.07846

Learning Skateboarding for Humanoid Robots through Massively Parallel Reinforcement Learning

William Thibault·Vidyasagar Rajendran·William Melek·Katja Mombaur

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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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William Thibault, Vidyasagar Rajendran, William Melek, Katja Mombaur. 2024-09-12. Learning Skateboarding for Humanoid Robots through Massively Parallel Reinforcement Learning. https://arxiv.org/abs/2409.07846

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