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dc.contributor.authorGeorge Thuruthel, Thomas
dc.contributor.authorGardner, Paul
dc.contributor.authorIida, Fumiya
dc.date.accessioned2022-01-12T00:30:21Z
dc.date.available2022-01-12T00:30:21Z
dc.date.issued2022-04-19
dc.identifier.issn2169-5172
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/332631
dc.description.abstractEmbedded soft sensors can significantly impact the design and control of soft-bodied robots. Although there have been considerable advances in technology behind these novel sensing materials, their application in real-world tasks, especially in closed-loop control tasks, has been severely limited. This is mainly because of the challenge involved with modeling a nonlinear time-variant sensor embedded in a complex soft-bodied system. This article presents a learning-based approach for closed-loop force control with embedded soft sensors and recurrent neural networks (RNNs). We present learning protocols for training a class of RNNs called long short-term memory (LSTM) that allows us to develop accurate and robust state estimation models of these complex dynamical systems within a short period of time. Using this model, we develop a simple feedback force controller for a soft anthropomorphic finger even with significant drift and hysteresis in our feedback signal. Simulation and experimental studies are conducted to analyze the capabilities and generalizability of the control architecture. Experimentally, we are able to develop a closed-loop controller with a control frequency of 25 Hz and an average accuracy of 0.17 N. Our results indicate that current soft sensing technologies can already be used in real-world applications with the aid of machine learning techniques and an appropriate training methodology.
dc.publisherMary Ann Liebert Inc
dc.rightsAll Rights Reserved
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserved
dc.titleClosing the Control Loop with Time-Variant Embedded Soft Sensors and Recurrent Neural Networks.
dc.typeArticle
dc.publisher.departmentDepartment of Engineering
dc.date.updated2022-01-10T09:44:20Z
prism.publicationNameSoft Robot
dc.identifier.doi10.17863/CAM.80076
dcterms.dateAccepted2022-01-10
rioxxterms.versionofrecord10.1089/soro.2021.0012
rioxxterms.versionAM
dc.contributor.orcidGeorge Thuruthel, Thomas [0000-0003-0571-1672]
dc.contributor.orcidIida, Fumiya [0000-0001-9246-7190]
dc.identifier.eissn2169-5180
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Future and Emerging Technologies (FET) (828818)
cam.issuedOnline2022-04-19
cam.orpheus.successWed May 25 11:13:12 BST 2022 - Embargo updated*
cam.orpheus.counter6
cam.depositDate2022-01-10
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2023-04-19


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