This paper addresses a significant challenge in achieving collaborative tasks; how can a robot or multiple robots, endowed with a library of pre-learned primitive movements, generate multiple simultaneous coordinated robotic movements, adapting and optimizing those in the library, to complete one collaborative task? This work can thus be seen as a follow-up to the work with a motion presented as dynamic movement primitive (DMP) that now considers collaborative tasks and the existence of multiple robots/manipulators. Specifically, we start with a simple task using one DMP and extend it to accommodate the coordinated execution of multiple DMPs in robots with multiple manipulators or-alternatively-multiple robots with a single manipulator. We investigate mechanisms to jointly optimize multiple DMPs to perform one task in a coordinated fashion. The joint trajectory is built from initial DMPs learned for a single manipulator, and its optimization must comply with task-specific constraints. We illustrate the application of our approach both in a simulated environment and in a simulated and real Baxter robot.

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.

This paper proposes an end-to-end framework that can learn and decompose complex movements provided by a human demonstrator and generate new complex motions. Our approach analyzes the demonstration by the human expert and uses geometric criteria to decompose the observed movement into segments that are stored as dynamic movement primitives (DMPs) in a library. Then, given a new environment configuration, our system autonomously composes different motion primitives to construct an optimized trajectory that meets the constraints imposed by the new environment. Our system is therefore able to construct new DMPs to execute complex motions in environments that differ from the one where the original motions were taught. Our approach is also compatible with existing run-time obstacle avoidance approaches. We illustrate the application of our approach both in simulation and with a Baxter robot.