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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorArana-Arexolaleiba, Nestor
dc.contributor.otherSerrano Muñoz, Antonio
dc.contributor.otherChrysostomou, Dimitrios
dc.contributor.otherBogh, Simon
dc.date.accessioned2022-06-10T09:26:18Z
dc.date.available2022-06-10T09:26:18Z
dc.date.issued2021
dc.identifier.issn1433-3015en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=166627en
dc.identifier.urihttp://hdl.handle.net/20.500.11984/5601
dc.description.abstractRemanufacturing automation must be designed to be flexible and robust enough to overcome the uncertainties, conditions of the products, and complexities in the planning and operation of the processes. Machine learning methods, in particular reinforcement learning, are presented as techniques to learn, improve, and generalise the automation of many robotic manipulation tasks (most of them related to grasping, picking, or assembly). However, not much has been exploited in remanufacturing, in particular in disassembly tasks. This work presents the state of the art of contact-rich disassembly using reinforcement learning algorithms and a study about the generalisation of object extraction skills when applied to contact-rich disassembly tasks. The generalisation capabilities of two state-of-the-art reinforcement learning agents (trained in simulation) are tested and evaluated in simulation, and real world while perform a disassembly task. Results show that at least one of the agents can generalise the contact-rich extraction skill. Besides, this work identifies key concepts and gaps for the reinforcement learning algorithms’ research and application on disassembly tasks.en
dc.description.sponsorshipComisión Europeaes
dc.language.isoengen
dc.publisherSpringeren
dc.rights© The Author(s) 2021en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectCircular economyen
dc.subjectRemanufacturingen
dc.subjectDisassemblyen
dc.subjectRoboticsen
dc.subjectReinforcement learningen
dc.subjectContact-rich manipulationen
dc.titleLearning and generalising object extraction skill for contact-rich disassembly tasks: an introductory studyen
dc.typeinfo:eu-repo/semantics/articleen
dcterms.accessRightsinfo:eu-repo/semantics/openAccessen
dcterms.sourceThe International Journal of Advanced Manufacturing Technologyen
dc.description.versioninfo:eu-repo/semantics/publishedVersionen
local.contributor.groupRobótica y automatizaciónes
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1007/s00170-021-08086-zen
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.otherinstitutionAalborg Universityes


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International