Recent Advances in Robot Learning from Demonstration
- Ravichandar, Harish Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, Georgia 30332, USA;,
- Polydoros, Athanasios S. Learning Algorithms and Systems Laboratory, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland;,
- Chernova, Sonia Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, Georgia 30332, USA;,
- Billard, Aude Learning Algorithms and Systems Laboratory, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland;,
Published in:
- Annual Review of Control, Robotics, and Autonomous Systems. - Annual Reviews. - 2020, vol. 3, no. 1, p. 297-330
English
In the context of robotics and automation, learning from demonstration (LfD) is the paradigm in which robots acquire new skills by learning to imitate an expert. The choice of LfD over other robot learning methods is compelling when ideal behavior can be neither easily scripted (as is done in traditional robot programming) nor easily defined as an optimization problem, but can be demonstrated. While there have been multiple surveys of this field in the past, there is a need for a new one given the considerable growth in the number of publications in recent years. This review aims to provide an overview of the collection of machine-learning methods used to enable a robot to learn from and imitate a teacher. We focus on recent advancements in the field and present an updated taxonomy and characterization of existing methods. We also discuss mature and emerging application areas for LfD and highlight the significant challenges that remain to be overcome both in theory and in practice.
- Language
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- English
- Open access status
- green
- Identifiers
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- DOI 10.1146/annurev-control-100819-063206
- ISSN 2573-5144
- Persistent URL
- https://sonar.ch/global/documents/281570
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