Learning to Generalize from Demonstrations

Learning by demonstration is a natural approach that can be used to build a robot’s task repertoire. In this paper we propose an algorithm that enables a learner to generalize a task representation from a small number of demonstrations of the same task. The algorithm can generalize a wide range of situations that typically occur in daily tasks. The paper also describes the supporting representation that we use in order to encode the generalized representation. The approach is validated with experimental results on a broad range of generalizations.