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

TL;DR

This paper introduces a comprehensive framework for full-body motion imitation in humanoid robots, combining contact-aware retargeting and predictive control to achieve accurate, balanced, and adaptable human-like movements in real time.

Contribution

It presents a novel whole-body motion imitation framework that effectively handles kinematic differences and balance challenges using contact-aware retargeting and model predictive control.

Findings

Successful imitation of diverse human motions in simulation and real robot

Demonstrated real-time balance maintenance and disturbance rejection

Validated improved motion accuracy and adaptability

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.