文章基本信息

标题：Deep Reinforcement Learning for Snake Robot Locomotion
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作者：Junyao Shi ; Tony Dear ; Scott David Kelly 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：9688-9695
DOI：10.1016/j.ifacol.2020.12.2619
语种：English
出版社：Elsevier
摘要：AbstractThe design of gaits for underactuated robots is often unintuitive, with many results derived from either trial and error or simplification of system structure. Recent advances in deep reinforcement learning have yielded results for systems continuous in either states or actions, which may extend to a variety of locomoting robots. In this work we employ reinforcement learning to derive efficient and novel gaits for both terrestrial and aquatic multi-link snake robots. Although such systems operate in different environments, we show that their shared geometric structure allows us to utilize the same learning techniques in both cases to find gaits without any human input. We present results learned and rolled out in simulation, and we describe preliminary efforts to implement the entire learning process on a physical system.
关键词：KeywordsMobile robotsAutonomous robotic systemsIntelligent robotics