Manipulation of free-floating objects using Faraday flows and deep reinforcement learning.
Nature Publishing Group
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Hardman, D., George Thuruthel, T., & Iida, F. (2022). Manipulation of free-floating objects using Faraday flows and deep reinforcement learning.. Sci Rep https://doi.org/10.1038/s41598-021-04204-9
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.
SHERO project, a Future and Emerging Technologies (FET) programme of the European Commission (grant agreement ID 828818)
European Commission Horizon 2020 (H2020) Future and Emerging Technologies (FET) (828818)
External DOI: https://doi.org/10.1038/s41598-021-04204-9
This record's URL: https://www.repository.cam.ac.uk/handle/1810/331630
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/
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