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

• Published in: IEEE transactions on industrial informatics 2017-06, Vol.13 (3), p.1099-1108
• Main Authors: Hwang, Kao-Shing, Jiang, Wei-Cheng, Chen, Yu-Jen, Shi, Haobin
• Format: Article
• Language: English
• Subjects: Algorithms; Clustering; Clustering algorithms; Clustering methods; Computer simulation; Dynamics; Human behavior; Human motion; Humanoid; Humanoid robots; imitation learning; key postures; Kinematics; Knots; Learning (artificial intelligence); Machine learning; Motion stability; reinforcement learning; Robot kinematics; Robots; Segmentation; Similarity; Static stability; Trajectories; Transits
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2017.2647993

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.