Multi-Speed Walking Gait Generation for Bipedal Robots Based on Reinforcement Learning and Human Motion Imitation

Gait generation is significant for a humanoid robot to realize flexible motion adapt to complex environments. With the wide applications of data-driven methods, gait generation approaches based on reinforcement learning have been represented. In this study, we proposed a multi-speed walking gait generation method based on reinforcement learning and human motion imitation. Multi-speed walking gaits were generated with imitation of human walking of only one speed. Moreover, we also analyzed multi-speed walking gait generation for a biped robot with reduced human motion data (e.g. motion data of trunk orientation and hip and knee angles without ankle angle, or motion data of trunk orientation and hip angle only) and reduced training time. This study provides a novel method for generation of multi-speed and human-like walking gaits of biped robot.