An Optimization-based Scheme for Real-time Transfer of Human Arm Motion to Robot Arm

摘要

Performing human-like motion is crucial for service humanoid robots. Real-time motion retargeting allows clear observation of the robot’s pose and provides instant feedback during human demonstrator actions. This paper presents an optimization-based real-time anthropomorphic motion retargeting framework for transferring human arm motion to a robot arm. The framework is generic, applicable to both spherical-rotational-spherical (SRS) and non-SRS robot arms. We introduce the normalized normal vector of the arm plane as an anthropomorphic criterion within our framework. The method is validated on a service humanoid robot, with both static and dynamic evaluations. The statistical analysis show that our method maintains strong anthropomorphic features while ensuring accurate wrist pose tracking.