Robotics research is increasingly addressing the issue of enabling robots to learn in social interaction. In contrast to the traditional approach by which robots are programmed by experts and prepared for and restricted to one specific purpose, they are now envisioned as general-purpose machines that should be able to carry out different tasks and thus solve various problems in everyday environments. Robots which are able to learn novel actions in social interaction with a human tutor would have many advantages. Unexperienced users could "program" new skills for a robot simply by demonstrating them.

Children are able to rapidly learn in social interaction. Modifications in tutoring behavior toward children ("motionese") are assumed to assist their learning processes. Similar to small children, robots do not have much experience of the world and thus could make use of this beneficial natural tutoring behavior if it was employed, when tutoring them.

To achieve this goal, the thesis provides theoretical background on imitation learning as a central field of social learning, which has received much attention in robotics and develops new interdisciplinary methods to measure interactive behavior. Based on this background, tutoring behavior is examined in adult-child, adult-adult, and adult-robot interactions by applying the developed methods. The findings reveal that the learner’s feedback is a constituent part of the natural tutoring interaction and shapes the tutor’s demonstration behavior.

The work provides an insightful understanding of interactional patterns and processes. From this it derives feedback strategies for human-robot tutoring interactions, with which a robot could prompt hand movement modifications during the tutor’s action demonstration by using its gaze, enabling robots to elicit advantageous modifications of the tutor’s behavior.