On Flexible Trajectory Description for Effective Rigid Body Motion Reproduction and Recognition

Recognizing and reproducing spatiotemporal motions are necessary when analyzing behaviors and movements during human-robot interaction. Rigid body motion trajectories are proven as compact and informative clues in characterizing motions. A flexible dual square-root function (DSRF) descriptor for representing rigid body motion trajectories, which can offer robustness in the description over raw data, was proposed in our previous study. However, this study focuses on exploring the application of the DSRF descriptor for effective backward motion reproduction and motion recognition. Specifically, two DSRF-based reproduction methods are initially proposed, including the recursive reconstruction and online optimization. New trajectories with novel situations and contextual information can be reproduced from a single demonstration while preserving the similarities with the original demonstration. Furthermore, motion recognition based on DSRF descriptor can be achieved by employing a template matching method. Finally, the experimental results demonstrate the effectiveness of the proposed method for rigid body motion reproduction and recognition.