Host: The Japan Society of Mechanical Engineers
Name : [in Japanese]
Date : June 06, 2021 - June 08, 2021
Snake-like robots are expected to be utilized for disaster rescue because they can locomote through gaps that humans cannot go through by appropriately coordinating the large degrees of freedom (DoFs) in their bodies. However, when learning locomotion using reinforcement learning (RL), an increase in learning time due to the large degrees of actions based on the bodily DoFs is a serious issue. To solve this, inspired by biomechanics on snake locomotion, we propose a reduction method for actions on model-based RL. In this paper, we verify the validity of the proposed method using an actual snake-like robot.