Learning a Faster Locomotion Gait for a Quadruped Robot with Model-Free Deep Reinforcement Learning

Abstract

Quadruped robots have great agility, flexibility and stability, which enables them to walk through uneven terrain. Motion control of legged robots is always a difficult problem. Previous approaches mostly either use a predefined gait that results in clumsy and unnatural behavior, or use reinforcement learning approach to generate a gait strategy that needs longtime computation and elegant network design. In this paper, we present an effective approach that uses deep reinforcement learning with prior knowledge to optimize the gait of quadruped robot. By using a specific quadruped robot walking gait as a priori knowledge, this approach adopts the technique of distributed proximal policy optimization to optimize the search for better gait. The proposed approach does not require modelling of complex robots, and has good network convergence speed and learning effect. Simulation results demonstrate that our proposed approach converges faster than other deep reinforcement learning methods without prior knowledge. Besides, our achieved gait has higher speed that is 50% faster than the trot gait without optimization.