Motion trajectories for robotic task teaching

Abstract

Motion trajectories play an important role in characterizing human or robot actions and behaviours. Effective ways to track and describe them are lacking that can fully depict spatial trajectories in 3D. In this work, I will present our research in visual tracking and trajectory description. An invariant descriptor proposed for free form trajectory description in Euclidean space. The signature admits rich invariants due to the computational locality. By implementing the approximate signature, the noise-sensitive high order derivatives are avoided. The trajectory can then be recognized based on the customized signatures similarity metric. Case studies will be given to show the signature’s effectiveness and robustness in 3-D trajectory description and recognition.

Biography

You-Fu Li received the PhD degree in robotics from the Department of Engineering Science, University of Oxford in 1993. From 1993 to 1995 he was a research staff in the Department of Computer Science at the University of Wales, Aberystwyth, UK. He joined City University of Hong Kong in 1995 and is currently professor in the Department of Mechanical and Biomedical Engineering. His research interests include robot sensing, robot vision, and visual tracking. He has served as an Associate Editor for IEEE Transactions on Automation Science and Engineering (T-ASE), Associate Editor and Guest Editor for IEEE Robotics and Automation Magazine (RAM), and Editor for CEB, IEEE International Conference on Robotics and Automation (ICRA).