Movement recognition and prediction using DMPs

Abstract

This paper proposes an approach for (a) recognizing an observed trajectory from a library of pre-learned motions; and (b) predicting the target position of such trajectory. In our approach, motions are represented as Dynamic Movement Primitives (DMPs). We use critical points from the observed trajectory to time-align it with those in the library. To match the observed trajectory with those in the library, we compare the changes in velocity orientation between consecutive critical points. The proposed approach is computationally light and, as such, can be performed at execution time. As for the prediction, we adopt a similar approach: after matching the observed trajectory to one in the library, we use the latter to predict the target point, modulating it to match the observed trajectory. Both recognition and prediction approaches are probabilistic and, as such, provide a measure of certainty/uncertainty in the recognition/prediction process. Such a measure of uncertainty is important in tasks involving human-robot collaboration, as it allows the robot to decide when it is sufficiently certain to act conditioned on the estimated trajectory. We illustrate our approach both in simulation and in a human-robot interaction scenario involving the Baxter robot.