Gait Generation for a Hexapod Robot Using Deep Reinforcement Learning

Abstract

In recent years, various approaches have been made in the control of robots using machine learning. Among them, research on deep reinforcement learning, which combines deep learning and reinforcement learning, has progressed. This learning method enables the learning of continuous behaviors by storing discretized Q-tables in neural networks. Examples of its application include tripod gait generation for quadruped robots and trajectory planning for industrial manipulators, contributing to learning complex behaviors. In this study, we focus on deep reinforcement learning for gait generation, specifically aiming to achieve forward locomotion in a hexapod robot using the tripod gait with three-point support．