An Experiment in Morphological Development for Learning ANN Based Controllers

UDC.coleccionInvestigaciónes_ES
UDC.conferenceTitleInternational Joint Conference on Neural Networks (IJCNN)es_ES
UDC.departamentoCiencias da Computación e Tecnoloxías da Informaciónes_ES
UDC.endPage8es_ES
UDC.grupoInvGrupo Integrado de Enxeñaría (GII)es_ES
UDC.institutoCentroCITENI - Centro de Investigación en Tecnoloxías Navais e Industriaises_ES
UDC.startPage1es_ES
UDC.volume2020es_ES
dc.contributor.authorNaya-Varela, M.
dc.contributor.authorFaíña, José Andrés
dc.contributor.authorDuro, Richard J.
dc.date.accessioned2024-09-30T13:51:35Z
dc.date.available2024-09-30T13:51:35Z
dc.date.issued2020
dc.descriptionThis version of the paper has been accepted for publication. The final published paper is available online at: https://doi.org/10.1109/IJCNN48605.2020.9206749.es_ES
dc.descriptionPublished in: 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 19-24 July 2020.es_ES
dc.description.abstract[Abstract]: Morphological development is part of the way any human or animal learns. The learning processes starts with the morphology at birth and progresses through changing morphologies until adulthood is reached. Biologically, this seems to facilitate learning and make it more robust. However, when this approach is transferred to robotic systems, the results found in the literature are inconsistent: morphological development does not provide a learning advantage in every case. In fact, it can lead to poorer results than when learning with a fixed morphology. In this paper we analyze some of the issues involved by means of a simple, but very informative experiment in quadruped walking. From the results obtained an initial series of insights on when and under what conditions to apply morphological development for learning are presented.es_ES
dc.description.sponsorshipThis work has been partially funded by the Ministerio de Ciencia, Innovación y Universidades of Spain/FEDER (grant RTI2018-101114-B-I00), Xunta de Galicia and FEDER (grant ED431C 2017/12) and M. Naya-Varela is very grateful for the support of the UDC-Inditex 2019 grant for international mobility. We also want to thank CESGA (Centro de Supercomputación de Galicia. https://www.cesga.es/) for the possibility of using its resources.es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2017/12es_ES
dc.identifier.citationM. Naya-Varela, A. Faina and R. J. Duro, "An Experiment in Morphological Development for Learning ANN Based Controllers," 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020, pp. 1-8, doi: 10.1109/IJCNN48605.2020.9206749es_ES
dc.identifier.doi10.1109/IJCNN48605.2020.9206749
dc.identifier.isbn978-1-7281-6926-2
dc.identifier.issn2161-4407
dc.identifier.urihttp://hdl.handle.net/2183/39309
dc.language.isoenges_ES
dc.publisherInstitute of Electrical and Electronics Engineerses_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-101114-B-I00/ES/ ARQUITECTURA COGNITIVA PARA ROBOTS CON ADAPTACION DE COMPORTAMIENTO AUTONOMAMENTE MOTIVADAes_ES
dc.relation.urihttps://doi.org/10.1109/IJCNN48605.2020.9206749es_ES
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.es_ES
dc.rights.accessRightsopen accesses_ES
dc.subjectCognitive roboticses_ES
dc.subjectMorphological development (MD)es_ES
dc.subjectQuadrupedal walkinges_ES
dc.titleAn Experiment in Morphological Development for Learning ANN Based Controllerses_ES
dc.typeconference outputes_ES
dspace.entity.typePublication
relation.isAuthorOfPublicationa3082627-8669-4257-8e06-9d5155b5bb31
relation.isAuthorOfPublicatione575435a-d72b-4442-a1f5-0a7c4b3f6871
relation.isAuthorOfPublication85df8d3f-49d3-4327-811d-e8038cead7dd
relation.isAuthorOfPublication.latestForDiscoverya3082627-8669-4257-8e06-9d5155b5bb31

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