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 [BibTeX] [Marc21]
Learning the Stiffness of a Continuous Soft Manipulator from Multiple Demonstrations
Type of publication: Book chapter
Citation: Bruno_SPRINGER_2015
Booktitle: Intelligent Robotics and Applications
Edition: Liu, H. and Kubota, N. and Zhu, X. and Dillmann, R. and Zhou, D.
Series: Lecture Notes in Computer Science
Volume: 9246
Year: 2015
Pages: 185-195
Publisher: Springer
Note: Best Paper Award Finalist at ICIRA'2015
ISBN: 978-3-319-22872-3
URL: http://dx.doi.org/10.1007/978-...
DOI: 10.1007/978-3-319-22873-0_17
Abstract: Continuous soft robots are becoming more and more widespread in applications, due to their increased safety and flexibility in critical applications. The possibility of having soft robots that are able to change their stiffness in selected parts can help in situations where higher forces need to be applied. This paper describes a theoretical framework for learning the desired stiffness characteristics of the robot from multiple demonstrations. The framework is based on a statistical mathematical model for encoding the motion of a continuous manipulator, coupled with an optimal control strategy for learning the best impedance parameters of the manipulator.
Keywords: continuum robots, minimal intervention control, robot learning
Projects Idiap
Authors Bruno, D.
Calinon, Sylvain
Malekzadeh, M. S.
Caldwell, D. G.
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  • Bruno_SPRINGER_2015.pdf