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arXiv · 2508.00362

A Whole-Body Motion Imitation Framework from Human Data for Full-Size Humanoid Robot

Zhenghan Chen·Haodong Zhang·Dongqi Wang·Jiyu Yu·Haocheng Xu·Yue Wang·Rong Xiong
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Abstract

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.

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Zhenghan Chen, Haodong Zhang, Dongqi Wang, Jiyu Yu, Haocheng Xu, Yue Wang, Rong Xiong. 2025-08-01. A Whole-Body Motion Imitation Framework from Human Data for Full-Size Humanoid Robot. https://arxiv.org/abs/2508.00362

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