Active Teaching in Robot Programming by Demonstration (Ro- Man'2007 best paper award)

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 new manipulation skills are transferred to a humanoid robot by using 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 by embodying the robot and putting it through the motion (kinesthetic teaching).

Publié dans:
Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (Ro-Man), 702-707
Présenté à:
RO-MAN'2007, Jeju, Korea, 2007
RO-MAN'07 Best paper award

Note: Le statut de ce fichier est: Anyone

 Notice créée le 2007-11-14, modifiée le 2020-07-30

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