用强化学习让机器人学滑板，训练速度更快。
Learning Skateboarding for Humanoid Robots through Massively Parallel Reinforcement Learning
- 基于周期性奖励机制，将行走算法扩展到滑板任务
- 在模拟中实现滑板动作，硬件实验正在进行
- 采用Brax/MJX加速训练，适合机器人运动控制研究者
基于学习的方法在生成机器人复杂运动方面已证明有效，包括类人机器人。强化学习（RL）已被用于学习运动策略，其中一些方法采用周期性奖励设计。本文将周期性奖励机制从行走任务拓展至滑板运动，针对REEM-C机器人进行实现。使用Brax/MJX框架构建强化学习问题，以实现快速训练。初步仿真结果已展示，硬件实验仍在进行中。
原文摘要 · Abstract (English)
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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