Motion Segmentation and Balancing for a Biped Robot's Imitation Learning

Abstract

Techniques for transferring human behaviors to robots through learning by imitation/demonstration have been the subject of much study. However, direct transfer of human motion trajectories to humanoid robots does not result in dynamically stable robot movements because of the differences in human and humanoid robot kinematics and dynamics. An imitating algorithm called posture-based imitation with balance learning (Post-BL) is proposed in this paper. This Post-BL algorithm consists of three parts: a key posture identification method is used to capture key postures as knots to reconstruct the motion imitated; a clustering method classifies key postures with high similarity; and a learning method enhances the static stability of balance during imitation. In motion reproduction, the proposed system smoothly transits between key poses and the robot learns to maintain balance by slightly adjusting the leg joints. The developed balance controller uses a reinforcement learning mechanism, which is sufficient to stabilize the robot during online imitation. The experimental results for simulation and a real humanoid robot show that the Post-BL algorithm allows demonstrated motions to be imitated balance to be preserved.