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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorArana-Arexolaleiba, Nestor
dc.contributor.otherElguea Aguinaco, Iñigo
dc.contributor.otherSerrano Muñoz, Antonio
dc.contributor.otherChrysostomou, Dimitrios
dc.contributor.otherInziarte Hidalgo, Ibai
dc.contributor.otherBogh, Simon
dc.date.accessioned2022-11-29T10:51:11Z
dc.date.available2022-11-29T10:51:11Z
dc.date.issued2022
dc.identifier.issn2076-3417en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=170351en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5895
dc.description.abstractThe introduction of collaborative robots in industrial environments reinforces the need to provide these robots with better cognition to accomplish their tasks while fostering worker safety without entering into safety shutdowns that reduce workflow and production times. This paper presents a novel strategy that combines the execution of contact-rich tasks, namely disassembly, with real-time collision avoidance through machine learning for safe human-robot interaction. Specifically, a goal-conditioned reinforcement learning approach is proposed, in which the removal direction of a peg, of varying friction, tolerance, and orientation, is subject to the location of a human collaborator with respect to a 7-degree-of-freedom manipulator at each time step. For this purpose, the suitability of three state-of-the-art actor-critic algorithms is evaluated, and results from simulation and real-world experiments are presented. In reality, the policy’s deployment is achieved through a new scalable multi-control framework that allows a direct transfer of the control policy to the robot and reduces response times. The results show the effectiveness, generalization, and transferability of the proposed approach with two collaborative robots against static and dynamic obstacles, leveraging the set of available solutions in non-monotonic tasks to avoid a potential collision with the human worker.en
dc.description.sponsorshipComisión Europeaes
dc.description.sponsorshipGobierno Vasco-Eusko Jaurlaritzaes
dc.language.isoengen
dc.publisherMDPIen
dc.rights© 2022 The Authorsen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectcollaborative robotsen
dc.subjectmachine learningen
dc.subjectreinforcement learningen
dc.subjectcontact-rich tasksen
dc.subjectDisassemblyen
dc.subjectcollision avoidanceen
dc.titleGoal-Conditioned Reinforcement Learning within a Human-Robot Disassembly Environmenten
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceApplied Sciencesen
local.contributor.groupRobótica y automatizaciónes
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.3390/app122211610en
local.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020-ECSEL/no 876852/EU/Verification and Validation of Automated Systems’ Safety and Security/VALU3Sen
local.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/857061/EU/Networking for research and development of human interactive and sensitive robotics taking advantage of additive manufacturing/R2P2en
local.relation.projectIDBikaintek 2020en
local.rights.publicationfeeAPCen
local.rights.publicationfeeamount2090.93€en
local.contributor.otherinstitutionhttps://ror.org/04m5j1k67en
local.contributor.otherinstitutionElectrotecnica Alavesa S.L.es
local.source.detailsVol. 12. Nº 22. Article 11610. November, 2022en
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85en


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Attribution 4.0 International
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