Exploring Transformers and Visual Transformers for Force Prediction in Human-Robot Collaborative Transportation Tasks

In this paper, we analyze the possibilities offered by Deep Learning State-of-the-Art architectures such as Transformers and Visual Transformers in generating a prediction of the human's force in a Human-Robot collaborative object transportation task at a middle distance. We outperform our prev...

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Detalhes bibliográficos
Autores: Domínguez Vidal, José Enrique, Sanfeliu, Alberto
Tipo de documento: artigo
Estado:Versión aceptada para publicación
Data de publicação:2024
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositório:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/387889
Acesso em linha:http://hdl.handle.net/10261/387889
https://api.elsevier.com/content/abstract/scopus_id/85202434002
Access Level:Acceso aberto
Palavra-chave:Force Prediction
Human-in-the-Loop
Object Transportation
Physical
Human-Robot Interaction
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spelling Exploring Transformers and Visual Transformers for Force Prediction in Human-Robot Collaborative Transportation TasksDomínguez Vidal, José EnriqueSanfeliu, AlbertoForce PredictionHuman-in-the-LoopObject TransportationPhysicalHuman-Robot InteractionIn this paper, we analyze the possibilities offered by Deep Learning State-of-the-Art architectures such as Transformers and Visual Transformers in generating a prediction of the human's force in a Human-Robot collaborative object transportation task at a middle distance. We outperform our previous predictor by achieving a success rate of 93.8% in testset and 90.9% in real experiments with 21 volunteers predicting in both cases the force that the human will exert during the next 1 s. A modification in the architecture allows us to obtain a second output from the model with a velocity prediction, which allows us to improve the capabilities of our predictor if it is used to estimate the trajectory that the human-robot pair will follow. An ablation test is also performed to verify the relative contribution to performance of each input.Work supported under the European project CANOPIES (H2020- ICT-2020-2-101016906) and by JST Moonshot R & D Grant Number: JPMJMS2011-85. The first author acknowledges Spanish FPU grant with ref. FPU19/06582.Peer reviewedInstitute of Electrical and Electronics EngineersEuropean CommissionDomínguez Vidal, José Enrique [0000-0002-0397-9248]Sanfeliu, Alberto [0000-0002-6018-154X]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttp://hdl.handle.net/10261/387889https://api.elsevier.com/content/abstract/scopus_id/85202434002reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/H2020/101016906https://doi.org/10.1109/ICRA57147.2024.10611205Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3878892026-05-22T06:33:51Z
dc.title.none.fl_str_mv Exploring Transformers and Visual Transformers for Force Prediction in Human-Robot Collaborative Transportation Tasks
title Exploring Transformers and Visual Transformers for Force Prediction in Human-Robot Collaborative Transportation Tasks
spellingShingle Exploring Transformers and Visual Transformers for Force Prediction in Human-Robot Collaborative Transportation Tasks
Domínguez Vidal, José Enrique
Force Prediction
Human-in-the-Loop
Object Transportation
Physical
Human-Robot Interaction
title_short Exploring Transformers and Visual Transformers for Force Prediction in Human-Robot Collaborative Transportation Tasks
title_full Exploring Transformers and Visual Transformers for Force Prediction in Human-Robot Collaborative Transportation Tasks
title_fullStr Exploring Transformers and Visual Transformers for Force Prediction in Human-Robot Collaborative Transportation Tasks
title_full_unstemmed Exploring Transformers and Visual Transformers for Force Prediction in Human-Robot Collaborative Transportation Tasks
title_sort Exploring Transformers and Visual Transformers for Force Prediction in Human-Robot Collaborative Transportation Tasks
dc.creator.none.fl_str_mv Domínguez Vidal, José Enrique
Sanfeliu, Alberto
author Domínguez Vidal, José Enrique
author_facet Domínguez Vidal, José Enrique
Sanfeliu, Alberto
author_role author
author2 Sanfeliu, Alberto
author2_role author
dc.contributor.none.fl_str_mv European Commission
Domínguez Vidal, José Enrique [0000-0002-0397-9248]
Sanfeliu, Alberto [0000-0002-6018-154X]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Force Prediction
Human-in-the-Loop
Object Transportation
Physical
Human-Robot Interaction
topic Force Prediction
Human-in-the-Loop
Object Transportation
Physical
Human-Robot Interaction
description In this paper, we analyze the possibilities offered by Deep Learning State-of-the-Art architectures such as Transformers and Visual Transformers in generating a prediction of the human's force in a Human-Robot collaborative object transportation task at a middle distance. We outperform our previous predictor by achieving a success rate of 93.8% in testset and 90.9% in real experiments with 21 volunteers predicting in both cases the force that the human will exert during the next 1 s. A modification in the architecture allows us to obtain a second output from the model with a velocity prediction, which allows us to improve the capabilities of our predictor if it is used to estimate the trajectory that the human-robot pair will follow. An ablation test is also performed to verify the relative contribution to performance of each input.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/387889
https://api.elsevier.com/content/abstract/scopus_id/85202434002
url http://hdl.handle.net/10261/387889
https://api.elsevier.com/content/abstract/scopus_id/85202434002
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC/H2020/101016906
https://doi.org/10.1109/ICRA57147.2024.10611205
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
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