Inferring what to imitate in manipulation actions by using a recommender system

Learning from demonstrations is an intuitive way for instructing robots by non-experts. One challenge in learning from demonstrations is to infer what to imitate, especially when the robot only observes the teacher and does not have further knowledge about the demonstrated actions. In this paper, we present a novel approach to the problem of inferring what to imitate to successfully reproduce a manipulation action based on a small number of demonstrations. Our method employs techniques from recommender systems to include expert knowledge. It models the demonstrated actions probabilistically and formulates the problem of inferring what to imitate via model selection. We select an appropriate model for the action each time the robot has to reproduce it given a new starting condition. We evaluate our approach using data acquired with a PR2 robot and demonstrate that our method achieves high success rates in different scenarios.