Learning Skateboarding for Humanoid Robots through Massively Parallel Reinforcement Learning

William Thibault; Vidyasagar Rajendran; William Melek; Katja Mombaur

Learning Skateboarding for Humanoid Robots through Massively Parallel Reinforcement Learning

Robotics 2024-09-13 v1

Authors: William Thibault , Vidyasagar Rajendran , William Melek , Katja Mombaur

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.

Keywords

robot locomotion imitation learning reinforcement learning

Cite

@article{arxiv.2409.07846,
  title  = {Learning Skateboarding for Humanoid Robots through Massively Parallel Reinforcement Learning},
  author = {William Thibault and Vidyasagar Rajendran and William Melek and Katja Mombaur},
  journal= {arXiv preprint arXiv:2409.07846},
  year   = {2024}
}

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