Invariant signature description and trajectory reproduction for robot Learning by Demonstration

Abstract

In most reported works about robot learning by demonstration (LbD), the demonstration is normally limited to simple gestures or grasp actions. In this paper, motion trajectory oriented LbD is studied in which free form 3-D motion trajectory is extracted to characterize certain human demonstrations. We propose to build effective description to motion trajectories to be learned by a robot instead of learning the raw trajectory data. A novel signature descriptor is formulated which serves as a generic and invariant description for motion trajectories. More importantly, a trajectory reproduction algorithm based on the learned signature is investigated to enable a robot to repeat/follow the reproduced trajectory instance. Experiments are reported to show the signature description and the reproduction algorithm for further application to the LbD.