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Exploiting the dynamics of soft materials for machine learning

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)339-347
Number of pages9
JournalSoft Robotics
Volume5
Issue number3
Early online date30 Apr 2018
DOIs
DateAccepted/In press - 19 Feb 2018
DateE-pub ahead of print - 30 Apr 2018
DatePublished (current) - 1 Jun 2018

Abstract

Soft materials are increasingly utilized for various purposes in many engineering applications. These materials have been shown to perform a number of functions that were previously difficult to implement using rigid materials. Here, we argue that the diverse dynamics generated by actuating soft materials can be effectively used for machine learning purposes. This is demonstrated using a soft silicone arm through a technique of multiplexing, which enables the rich transient dynamics of the soft materials to be fully exploited as a computational resource. The computational performance of the soft silicone arm is examined through two standard benchmark tasks. Results show that the soft arm compares well to or even outperforms conventional machine learning techniques under multiple conditions. We then demonstrate that this system can be used for the sensory time series prediction problem for the soft arm itself, which suggests its immediate applicability to a real-world machine learning problem. Our approach, on the one hand, represents a radical departure from traditional computational methods, whereas on the other hand, it fits nicely into a more general perspective of computation by way of exploiting the properties of physical materials in the real world.

    Research areas

  • physical reservoir computing, soft robotics, physical computation, octopus

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    Rights statement: This is the final published version of the article (version of record). It first appeared online via Mary Ann Liebert, at https://www.liebertpub.com/doi/10.1089/soro.2017.0075 . Please refer to any applicable terms of use of the publisher.

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    Licence: CC BY

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