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dc.rights.licenseAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.contributor.authorElguea, Íñigo
dc.contributor.authorArana-Arexolaleiba, Nestor
dc.contributor.authorSerrano Muñoz, Antonio
dc.contributor.otherChrysostomou, Dimitrios
dc.contributor.otherInziarte Hidalgo, Ibai
dc.contributor.otherBogh, Simon
dc.date.accessioned2023-01-26T15:43:38Z
dc.date.available2023-01-26T15:43:38Z
dc.date.issued2023
dc.identifier.issn1879-2537en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=171346en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5968
dc.description.abstractResearch and application of reinforcement learning in robotics for contact-rich manipulation tasks have exploded in recent years. Its ability to cope with unstructured environments and accomplish hard-to-engineer behaviors has led reinforcement learning agents to be increasingly applied in real-life scenarios. However, there is still a long way ahead for reinforcement learning to become a core element in industrial applications. This paper examines the landscape of reinforcement learning and reviews advances in its application in contact-rich tasks from 2017 to the present. The analysis investigates the main research for the most commonly selected tasks for testing reinforcement learning algorithms in both rigid and deformable object manipulation. Additionally, the trends around reinforcement learning associated with serial manipulators are explored as well as the various technological challenges that this machine learning control technique currently presents. Lastly, based on the state-of-the-art and the commonalities among the studies, a framework relating the main concepts of reinforcement learning in contact-rich manipulation tasks is proposed. The final goal of this review is to support the robotics community in future development of systems commanded by reinforcement learning, discuss the main challenges of this technology and suggest future research directions in the domain.en
dc.description.sponsorshipComisión Europeaen
dc.language.isoengen
dc.publisherElsevieren
dc.rights© 2023 The Authorsen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectReinforcement learningen
dc.subjectContact-rich manipulationen
dc.subjectIndustrial manipulatorsen
dc.subjectRigid object manipulationen
dc.subjectDeformable object manipulationen
dc.titleA review on reinforcement learning for contact-rich robotic manipulation tasksen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceRobotics and Computer-Integrated Manufacturingen
local.contributor.groupRobótica y automatizaciónes
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1016/j.rcim.2022.102517en
local.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/8570617/EU/Networking for research and development of human interactive and sensitive robotics taking advantage of additive manufacturing/R2P2en
local.contributor.otherinstitutionElectrotecnica Alavesa S.L.es
local.contributor.otherinstitutionhttps://ror.org/04m5j1k67en
local.contributor.otherinstitutionMontajes Mantenimiento y Automatismos Eléctricos Navarraes
local.source.detailsVol. 81. N. artículo 102517en
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-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International