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Learning From Humans
Type of publication: Book chapter
Citation: Billard_SPRINGER_2016
Booktitle: Handbook of Robotics
Edition: 2nd Edition
Chapter: 74
Year: 2016
Pages: 1995-2014
Publisher: Springer
Address: Secaucus, NJ, USA
URL: http://link.springer.com/chapt...
DOI: 10.1007/978-3-319-32552-1_74
Abstract: 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.
Projects Idiap
Authors Billard, A. G.
Calinon, Sylvain
Dillmann, R.
Editors Siciliano, B.
Khatib, O.
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