Aude G. Billard, Sylvain Calinon and Rüdiger Dillmann
This chapter surveys the main approaches developed to date to endow robots with the ability to learn from human guidance. The field is best known as robot programming by demonstration, robot learning from/by demonstration, apprenticeship learning and imitation learning. We start with a brief historical overview of the field. We then summarize the various approaches taken to solve four main questions: when, what, who and when to imitate. We emphasize the importance of choosing well the interface and the channels used to convey the demonstrations, with an eye on interfaces providing force control and force feedback. We then review algorithmic approaches to model skills individually and as a compound and algorithms that combine learning from human guidance with reinforcement learning. We close with a look on the use of language to guide teaching and a list of open issues.
Active teaching
Author Maya Cakmak, Andrea Thomaz
Video ID : 107
Active-teaching scenario where the Simon humanoid robot asks for help during or after teaching, verifying that its understanding of the task is correct.
Reference: M. Cakmak, A.L. Thomaz: Designing robot learners that ask good questions, Proc. ACM/IEEE Int. Conf. Human-Robot Interaction (HRI), Boston (2012), pp. 17–24, URL: https://www.youtube.com/user/SimonTheSocialRobot .