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

Praeger, Matthew and Xie, Yunhui and Grant-Jacob, James A and Eason, Robert W and Mills, Ben (2021) Playing optical tweezers with deep reinforcement learning: in virtual, physical and augmented environments. Machine Learning: Science and Technology, 2 (3). 035024. ISSN 2632-2153

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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.

Item Type: Article
Subjects: STM Digital Press > Multidisciplinary
Depositing User: Unnamed user with email support@stmdigipress.com
Date Deposited: 09 Jul 2023 04:20
Last Modified: 18 May 2024 08:41
URI: http://publications.articalerewriter.com/id/eprint/1273

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