The University of Southampton
University of Southampton Institutional Repository

Playing optical tweezers with deep reinforcement learning: in virtual, physical and augmented environments

Playing optical tweezers with deep reinforcement learning: in virtual, physical and augmented environments
Playing optical tweezers with deep reinforcement learning: in virtual, physical and augmented environments
Reinforcement learning was carried out in a simulated environment to learn continuous velocity control over multiple motor axes. This was then applied to a real-world optical tweezers experiment with the objective of moving a laser-trapped microsphere to a target location whilst avoiding collisions with other free-moving microspheres. The concept of training a neural network in a virtual environment has significant potential in the application of machine learning for experimental optimization and control, as the neural network can discover optimal methods for problem solving without the risk of damage to equipment, and at a speed not limited by movement in the physical environment. As the neural network treats both virtual and physical environments equivalently, we show that the network can also be applied to an augmented environment, where a virtual environment is combined with the physical environment. This technique may have the potential to unlock capabilities associated with mixed and augmented reality, such as enforcing safety limits for machine motion or as a method of inputting observations from additional sensors.
optical tweezers, laser trapping, machine learning, reinforcement learning
2632-2153
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Xie, Yunhui
f2c3b0e4-8650-4e04-80e5-04505f45bdd6
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Xie, Yunhui
f2c3b0e4-8650-4e04-80e5-04505f45bdd6
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0

Praeger, Matthew, Xie, Yunhui, Grant-Jacob, James, Eason, R.W. and Mills, Benjamin (2021) Playing optical tweezers with deep reinforcement learning: in virtual, physical and augmented environments. Machine Learning: Science and Technology. (doi:10.1088/2632-2153/abf0f6). (In Press)

Record type: Article

Abstract

Reinforcement learning was carried out in a simulated environment to learn continuous velocity control over multiple motor axes. This was then applied to a real-world optical tweezers experiment with the objective of moving a laser-trapped microsphere to a target location whilst avoiding collisions with other free-moving microspheres. The concept of training a neural network in a virtual environment has significant potential in the application of machine learning for experimental optimization and control, as the neural network can discover optimal methods for problem solving without the risk of damage to equipment, and at a speed not limited by movement in the physical environment. As the neural network treats both virtual and physical environments equivalently, we show that the network can also be applied to an augmented environment, where a virtual environment is combined with the physical environment. This technique may have the potential to unlock capabilities associated with mixed and augmented reality, such as enforcing safety limits for machine motion or as a method of inputting observations from additional sensors.

Text
Author Submitted Manuscript - Author's Original
Restricted to Repository staff only
Request a copy

More information

Submitted date: 7 January 2021
Accepted/In Press date: 22 March 2021
Keywords: optical tweezers, laser trapping, machine learning, reinforcement learning

Identifiers

Local EPrints ID: 446998
URI: http://eprints.soton.ac.uk/id/eprint/446998
ISSN: 2632-2153
PURE UUID: 205b5c14-b21e-43fd-8fc4-a57d28bde943
ORCID for Matthew Praeger: ORCID iD orcid.org/0000-0002-5814-6155
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for R.W. Eason: ORCID iD orcid.org/0000-0001-9704-2204
ORCID for Benjamin Mills: ORCID iD orcid.org/0000-0002-1784-1012

Catalogue record

Date deposited: 01 Mar 2021 17:33
Last modified: 12 May 2021 01:44

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×