Optimize and Coordinate Multiple DMPs under Constraints to Achieve a Collaborative Manipulation Task

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.