Compound Movement Recognition Using Dynamic Movement Primitives

Abstract

This paper proposes a method for recognizing compound trajectories. Given a library of pre-learned movements described using dynamic movement primitives, our approach is able to break down an observed trajectory into its individual components, each of which is a segment of one of the movements in the library. We build on previous work that uses critical points for movement recognition and prediction, and extend it to handle trajectories comprising multiple segments from possibly different movements. Our approach assumes that each segment in the observed trajectory is either the initial segment of a new primitive or the continuation of the previous segment. Then, given a partial trajectory, our method is able to predict the most likely next target—i.e., the end-point of the movement currently being executed, if the latter is executed to the end. By using an effective search tree, our approach is able to run at execution time and provide an efficient way for action recognition and prediction, which has applications in human-robot interaction scenarios. We validate our approach both in simulation and in a human-robot interaction scenario involving the Baxter robot.