A Composite Control Strategy for Quadruped Robot by Integrating Reinforcement Learning and Model-Based Control

Locomotion in the wild requires the quadruped robot to have strong capabilities in adaptation and robustness. The deep reinforcement learning (DRL) exhibits the huge potential in environmental adaptability, while its stability issues remain open. On the other hand, the quadruped robot dynamic model contains a lot of useful information that is beneficial to the robust control. The combination of DRL with model-based control may take both strengths and hold promises in better robustness. In this paper, the DRL and the proposed model-based controller are firmly integrated in a novel manner such that the proposed model-based controller is able to rectify the gait commands generated by DRL based on the system dynamic model so as to enhance the robustness of the quadruped robot against the external disturbances. Besides, a potential energy function is introduced to achieve the compliant contact. The stability of the proposed method is ensured in terms of passivity analysis. Several physical experiments are carried out to verify the performance of the proposed method.