Personalized Motion Retargeting through Bidirectional Human-Robot Imitation

Abstract

This paper presents a novel approach to address personalized motion retargeting (PMR) from humans to robots. In contrast to most existing retargeting methods that overlook personalized human motion features, we propose a continuous learning framework that enables robots to learn and refine retargeting through human-robot interaction. Within the framework, we design a bidirectional imitation loop to encode the user's preferences: The robot imitates the human first, and then the human replicates the robot's movements. By being imitated, the robot gains insight into the effect of its own action, allowing the robot to adjust its retargeting accordingly. Furthermore, to enhance the interaction efficiency, our method significantly reduces training time by optimizing the robot's local behavior rather than learning from scratch, leading to rapid and responsive personalization in seconds.