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

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.