Feedback error learning for rhythmic motor primitives

Rhythmic motor primitives can be used to learn a variety of oscillatory behaviors from demonstrations or reward signals, e.g., hopping, walking, running and ball-bouncing. However, frequently, such rhythmic motor primitives lead to failures unless a stabilizing controller ensures their functionality, e.g., a balance controller for a walking gait. As an ideal oscillatory behavior requires the stabilizing controller only for exceptions, e.g., to prevent failures, we devise an online learning approach that reduces the dependence on the stabilizing controller. Inspired by related approaches in model learning, we employ the stabilizing controller's output as a feedback error learning signal for adapting the gait. We demonstrate the resulting approach in two scenarios: a rhythmic arm's movements and gait adaptation of an underactuated biped.