Robust Regression-Based Motion Perception for Online Imitation on Humanoid Robot

摘要

Kinect is frequently used as a capture device for perceiving human motion in human-robot interaction. However, the Kinect's principle of capture makes it possible for outliers to be present in the raw 3D joint position data, yielding an unsatisfying motion imitation by a humanoid robot. To eliminate these outliers and improve the precision of motion perception, we are inspired from the principle of signal restoration and propose a robust regression-based refining algorithm. We made contributions mainly in designing an Arc Tangent Square function to estimate the tendency of motion trajectories, and constructing a stepwise robust regression strategy to successively refine the outliers hidden in the motion capture data. The motion trajectories refined by the proposed algorithm are 40, 10, and 30% better than the raw motion capture data on spatial similarity, temporal similarity, and smoothness, respectively. In the online implementation on a humanoid robot NAO, the imitated motions of the human's upper limbs are synchronous and accurate. The proposed robust regression-based refining algorithm realizes high-performance motion perception for online imitation of the humanoid robot.