Manipulation of free-floating objects using Faraday flows and deep reinforcement learning
Publication Date
2022-12Journal Title
Scientific Reports
Publisher
Springer Science and Business Media LLC
Volume
12
Issue
1
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Hardman, D., George Thuruthel, T., & Iida, F. (2022). Manipulation of free-floating objects using Faraday flows and deep reinforcement learning. Scientific Reports, 12 (1) https://doi.org/10.1038/s41598-021-04204-9
Abstract
<jats:title>Abstract</jats:title><jats:p>The ability to remotely control a free-floating object through surface flows on a fluid medium can facilitate numerous applications. Current studies on this problem have been limited to uni-directional motion control due to the challenging nature of the control problem. Analytical modelling of the object dynamics is difficult due to the high-dimensionality and mixing of the surface flows while the control problem is hard due to the nonlinear slow dynamics of the fluid medium, underactuation, and chaotic regions. This study presents a methodology for manipulation of free-floating objects using large-scale physical experimentation and recent advances in deep reinforcement learning. We demonstrate our methodology through the open-loop control of a free-floating object in water using a robotic arm. Our learned control policy is relatively quick to obtain, highly data efficient, and easily scalable to a higher-dimensional parameter space and/or experimental scenarios. Our results show the potential of data-driven approaches for solving and analyzing highly complex nonlinear control problems.</jats:p>
Keywords
Article, /639/766/189, /639/766/530, /639/705/1042, article
Identifiers
s41598-021-04204-9, 4204
External DOI: https://doi.org/10.1038/s41598-021-04204-9
This record's URL: https://www.repository.cam.ac.uk/handle/1810/332578
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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