Speed adaptation for self-improvement of skills learned from user demonstrations

Abstract

SUMMARYThe paper addresses the problem of speed adaptation of movements subject to environmental constraints. Our approach relies on a novel formulation of velocity profiles as an extension of dynamic movement primitives (DMP). The framework allows for compact representation of non-uniformly accelerated motion as well as simple modulation of the movement parameters. In the paper, we evaluate two model free methods by which optimal parameters can be obtained: iterative learning control (ILC) and policy search based reinforcement learning (RL). The applicability of each method is discussed and evaluated on two distinct cases, which are hard to model using standard techniques. The first deals with hard contacts with the environment while the second process involves liquid dynamics. We find ILC to be very efficient in cases where task parameters can be easily described with an error function. On the other hand, RL has stronger convergence properties and can therefore provide a solution in the general case.