Motion capture based human motion recognition and imitation by direct marker control

This paper deals with the imitation of human motions by a humanoid robot based on marker point measurements from a 3D motion capture system. For imitating the human's motion, we propose a Cartesian control approach in which a set of control points on the humanoid is selected and the robot is virtually connected to the measured marker points via translational springs. The forces according to these springs drive a simplified simulation of the robot dynamics, such that the real robot motion can finally be generated based on joint position controllers effectively managing joint friction and other uncertain dynamics. This procedure allows to make the robot follow the marker points without the need of explicitly computing inverse kinematics. For the implementation of the marker control on a humanoid robot, we combine it with a center of gravity based balancing controller for the lower body joints. We integrate the marker control based motion imitation with the mimesis model, which is a mathematical model for motion learning, recognition, and generation based on hidden Markov models (HMMs). Learning, recognition, and generation of motion primitives are all performed in marker coordinates paving the way for extending these concepts to task space problems and object manipulation. Finally, an experimental evaluation of the presented concepts using a 38 degrees of freedom humanoid robot is discussed.