Inspired by biological motion generation, central pattern generators (CPGs) is frequently employed in legged robot locomotion control to produce natural gait pattern with low-dimensional control signals. However, the limited adaptability and stability over complex terrains hinder its application. To address this issue, this paper proposes a terrain-adaptive locomotion control method that incorporates deep reinforcement learning (DRL) framework into CPG, where the CPG model is responsible for the generation of synchronized signals, providing basic locomotion gait, while DRL is integrated to enhance the adaptability of robot towards uneven terrains by adjusting the parameters of CPG mapping functions. The experiments conducted on the hexapod robot in Isaac Gym simulation environment demonstrated the superiority of the proposed method in terrain-adaptability, convergence rate and reward design complexity.

翻译：受生物运动生成机制的启发，中央模式生成器（CPG）常被用于足式机器人运动控制，以通过低维控制信号生成自然步态模式。然而，在复杂地形上有限的适应性和稳定性制约了其应用。为解决此问题，本文提出一种将深度强化学习（DRL）框架融入CPG的地形自适应运动控制方法，其中CPG模型负责生成同步信号以提供基础运动步态，而DRL通过调整CPG映射函数参数增强机器人对非平坦地形的适应性。在Isaac Gym仿真环境中对六足机器人进行的实验表明，所提方法在地形适应性、收敛速度和奖励设计复杂度方面具有优越性。