Learning Skateboarding for Humanoid Robots through Massively Parallel Reinforcement Learning
<|reference_start|>Learning Skateboarding for Humanoid Robots through Massively Parallel Reinforcement Learning: 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.<|reference_end|>
@article{thibault2024learning,
    title={Learning Skateboarding for Humanoid Robots through Massively Parallel
  Reinforcement Learning},
    author={William Thibault, Vidyasagar Rajendran, William Melek, Katja Mombaur},
    journal={arXiv preprint arXiv:2409.07846},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07846},
    primaryClass={cs.RO}
}