Dynamic Movement Primitives for Moving Goals with Temporal Scaling Adaptation

Abstract

In this work, we propose an augmentation to the Dynamic Movement Primitives (DMP) framework which allows the system to generalize to moving goals without the use of any known or approximation model for estimating the goal's motion. We aim to maintain the demonstrated velocity levels during the execution to the moving goal, generating motion profiles appropriate for human robot collaboration. The proposed method employs a modified version of a DMP, learned by a demonstration to a static goal, with adaptive temporal scaling in order to achieve reaching of the moving goal with the learned kinematic pattern. Only the current position and velocity of the goal are required. The goal's reaching error and its derivative is proved to converge to zero via contraction analysis. The theoretical results are verified by simulations and experiments on a KUKA LWR4+ robot.