人間データからの全身運動模倣フレームワーク：フルサイズヒューマノイドロボット向け
A Whole-Body Motion Imitation Framework from Human Data for Full-Size Humanoid Robot
人間の全身運動を接触を考慮してリターゲティングし、非線形重心MPCと全身制御でバランスを保ちながら実機ヒューマノイドに模倣させるフレームワークを提案した。
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著者: Zhenghan Chen, Haodong Zhang, Dongqi Wang, Jiyu Yu, Haocheng Xu, Yue Wang, Rong Xiong
分類: cs.RO
原文アブストラクト
Motion imitation is a pivotal and effective approach for humanoid robots to achieve a more diverse range of complex and expressive movements, making their performances more human-like. However, the significant differences in kinematics and dynamics between humanoid robots and humans present a major challenge in accurately imitating motion while maintaining balance. In this paper, we propose a novel whole-body motion imitation framework for a full-size humanoid robot. The proposed method employs contact-aware whole-body motion retargeting to mimic human motion and provide initial values for reference trajectories, and the non-linear centroidal model predictive controller ensures the motion accuracy while maintaining balance and overcoming external disturbances in real time. The assistance of the whole-body controller allows for more precise torque control. Experiments have been conducted to imitate a variety of human motions both in simulation and in a real-world humanoid robot. These experiments demonstrate the capability of performing with accuracy and adaptability, which validates the effectiveness of our approach.
