強化学習によるヘビ型ロボットの省エネ蛇行歩容探索
Energy-Efficient Slithering Gait Exploration for a Snake-like Robot based on Reinforcement Learning
ヘビ型ロボットの省エネな蛇行歩容をPPOベースの強化学習で生成し、従来のパラメータ化歩容制御より自然で適応的かつ高効率であることを示した。
著者: Zhenshan Bing, Christian Lemke, Zhuangyi Jiang, Kai Huang, Alois Knoll
分類: cs.RO
原文アブストラクト
Similar to their counterparts in nature, the flexible bodies of snake-like robots enhance their movement capability and adaptability in diverse environments. However, this flexibility corresponds to a complex control task involving highly redundant degrees of freedom, where traditional model-based methods usually fail to propel the robots energy-efficiently. In this work, we present a novel approach for designing an energy-efficient slithering gait for a snake-like robot using a model-free reinforcement learning (RL) algorithm. Specifically, we present an RL-based controller for generating locomotion gaits at a wide range of velocities, which is trained using the proximal policy optimization (PPO) algorithm. Meanwhile, a traditional parameterized gait controller is presented and the parameter sets are optimized using the grid search and Bayesian optimization algorithms for the purposes of reasonable comparisons. Based on the analysis of the simulation results, we demonstrate that this RL-based controller exhibits very natural and adaptive movements, which are also substantially more energy-efficient than the gaits generated by the parameterized controller. Videos are shown at \textcolor{blue}{\href{https://videoviewsite.wixsite.com/rlsnake}{https://videoviewsite.wixsite.com/rlsnake}}.
