Active Teaching in Robot Programming by Demonstration

Robot programming by demonstration (RbD) covers methods by which a robot learns new skills through human guidance. In this work, we take the perspective that the role of the teacher is more important than just being a model of successful behaviour, and present a probabilistic framework for RbD which allows to extract incrementally the essential characteristics of a task described at a trajectory level. To demonstrate the feasibility of our approach, we present two experiments where manipulation skills are transferred to a humanoid robot by means of active teaching methods that put the human teacher in the loop of the robot´s learning. The robot first observes the task performed by the user (through motion sensors) and the robot´s skill is then refined progressively by embodying the robot and putting it through the motion (kinesthetic teaching).