Combined Reinforcement Learning and CPG Algorithm to Generate Terrain-Adaptive Gait of Hexapod Robots

Abstract

Terrain adaptation research can significantly improve the motion performance of hexapod robots. In this paper, we propose a method that combines reinforcement learning with a central pattern generator (CPG) to enhance the terrain adaptation of hexapod robots in terms of gait planning. The hexapod robot’s complex task presents a high-dimensional observation and action space, which makes it challenging to directly apply reinforcement learning to robot control. Therefore, we utilize the CPG algorithm to generate the rhythmic gait while compressing the action space dimension of the agent. Additionally, the proposed method requires less internal sensor information, which exhibits strong applicability. Finally, we conduct experiments and deploy the proposed framework in the simulation environment. The results show that the terrain adaptation policy trained in our framework enables the hexapod robot to move more smoothly and efficiently on rugged terrain compared to the traditional CPG method.