Directing Policy Search with Interactively Taught Via-Points

Policy search has been successfully applied to robot motor learning problems. However, for moderately complex tasks the necessity of good heuristics or initialization still arises. One method that has been used to alleviate this problem is to utilize demonstrations obtained by a human teacher as a starting point for policy search in the space of trajectories. In this paper we describe an alternative way of giving demonstrations as soft via-points and show how they can be used for initialization as well as for active corrections during the learning process. With this approach, we restrict the search space to trajectories that will be close to the taught via-points at the taught time and thereby significantly reduce the number of samples necessary to learn a good policy. We show with a simulated robot arm that our method can efficiently learn to insert an object in a hole with just a minimal demonstration and evaluate our method further on a synthetic letter reproduction task.