Gait Optimization Control of Spinal Quadruped Robot Based on Deep Reinforcement Learning

The spine enhances the flexibility of quadrupeds during locomotion. Inspired by this biological mechanism, this study incorporates an actuated spinal joint into a quadruped robot, enabling more natural motion and posture adjustment. To improve the motion stability of spinal robots in complex environments, a deep reinforcement learning framework that integrates a central pattern generator (CPG) with the twin delayed deterministic policy gradient (TD3) algorithm is proposed to optimize the gait motion of the spinal quadruped robot. First, the structure and parameters of the quadruped robot with a spinal joint are analyzed and a CPG coupling model incorporating spinal motion parameters is designed. Subsequently, a TD3-CPG algorithm framework based on a joint incremental strategy is proposed to optimize the robot's gait, exploring optimal control strategies for terrain adaptation through spinal motion integration. Finally, experiments are conducted on various obstacle terrains to validate the proposed algorithm. Simulation and experiment results demonstrate the effectiveness of the algorithm in optimizing the gait of the spinal quadruped robot, showing significant improvements in walking stability, speed, and terrain adaptability across different terrains.