Human-robot interaction for learning and adaptation of object movements

In this paper we present a new robot control and learning framework. By integrating previously presented as well as new methods, the robot is able to learn an invariant and generic movement representation from a human tutor. We argue that in order to apply such generic representations to new situations and thus create a flexible system, the use of interaction is beneficial. The interaction is based on a kinematically controlled model of a human tutor, which is used as a model-based filter and also for recognizing postures that influence the interaction. In addition, a new movement segmentation scheme is presented that is based on correlating movements by the tutor's hand with the salient objects in the scene. The focus of this paper is on the interactive learning aspects of the system and particular emphasis is given to an experiment in which the humanoid robot ASIMO learns from a human tutor. The system includes extensive generalization capabilities that result from an online adaption of the robot's body schema and the exploitation of inter-trial variance from multiple demonstrations. This enables the robot to reproduce the movement in new situations. For example, a stacking task that the tutor performed one-handed can be executed bi-manually by the robot.