Accelerating Synchronization of Movement Primitives: Dual-Arm Discrete-Periodic Motion of a Humanoid Robot

Human-demonstrated motion transferred to a robotic platform often needs to be adapted to the current state of the environment or to modified task requirements. Adaptation, i.e. learning of a modified behavior, needs to be fast to enable quick utilization of the robot either in industry or in future household-assistant tasks. In this paper we show how to accelerate trajectory adaptation based on learning of coupling terms in the framework of dynamic movement primitives (DMPs). Our method applies ideas from feedback error learning to iterative learning control (ILC). By taking into account the actual physical constraints of the synchronous motion - through synchronization of both positions (or forces) and velocities - it is not only a more faithful representation of actual real-world processes, but it also accelerates the speed of convergence. To show the applicability of the approach in the framework of DMPs, we tested it on a formulation which encodes an initial discrete motion, followed by a periodic behavior, all in a single system. Modifications of the original discrete-periodic formulation now also allow for the use of DMP temporal scaling property. In the paper we also show how the DMP coupling can be implemented in joint space, whereas the measured forces and previous approaches always remained in the task space. We applied our approach to an example dual-arm synchronization task on Sarcos humanoid robot CB-i.