Exploiting symmetries in reinforcement learning of bimanual robotic tasks

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Detalles Bibliográficos
Autores: Amadio, Fabio, Colomé Figueras, Adrià, Torras, Carme|||0000-0002-2933-398X
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/178327
Acceso en línea:https://hdl.handle.net/2117/178327
https://dx.doi.org/10.1109/LRA.2019.2898330
Access Level:acceso abierto
Palabra clave:Humanoid robots
Learning
Artificial intelligence
Manipulators
Classificació INSPEC::Cybernetics::Artificial intelligence
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Àrees temàtiques de la UPC::Informàtica::Robòtica
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oai_identifier_str oai:upcommons.upc.edu:2117/178327
network_acronym_str ES
network_name_str España
repository_id_str
spelling Exploiting symmetries in reinforcement learning of bimanual robotic tasksAmadio, FabioColomé Figueras, AdriàTorras, Carme|||0000-0002-2933-398XHumanoid robotsLearningArtificial intelligenceManipulatorsClassificació INSPEC::Cybernetics::Artificial intelligenceÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificialÀrees temàtiques de la UPC::Informàtica::Robòtica© 2019 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.Movement Primitives (MPs) have been widely adopted for representing and learning robotic movements using Reinforcement Learning Policy Search. Probabilistic Movement Primitives (ProMPs) are a kind of MP based on a stochastic representation over sets of trajectories, able of capturing the variability allowed while executing a movement. This approach has proved effective in learning a wide range of robotic movements, but it comes with the need of dealing with a high-dimensional space of parameters. This may be a critical problem when learning tasks with two robotic manipulators, and this work proposes an approach to reduce the dimension of the parameter space based on the exploitation of symmetry. A symmetrization method for ProMPs is presented and used to represent two movements, employing a single ProMP for the first arm and a symmetry surface that maps that ProMP to the second arm. This symmetric representation is then adopted in reinforcement learning of bimanual tasks (from user-provided demonstrations), using Relative Entropy Policy Search (REPS) algorithm. The symmetry-based approach developed has been tested in an experiment of cloth manipulation, showing a speed increment in learning the task.Peer ReviewedInstitute of Electrical and Electronics Engineers (IEEE)20192019-01-0120202020-02-21journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/178327https://dx.doi.org/10.1109/LRA.2019.2898330reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengEuropean Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 741930 CLOTH manIpulation Learning from DEmonstrationsopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1783272026-05-27T15:37:01Z
dc.title.none.fl_str_mv Exploiting symmetries in reinforcement learning of bimanual robotic tasks
title Exploiting symmetries in reinforcement learning of bimanual robotic tasks
spellingShingle Exploiting symmetries in reinforcement learning of bimanual robotic tasks
Amadio, Fabio
Humanoid robots
Learning
Artificial intelligence
Manipulators
Classificació INSPEC::Cybernetics::Artificial intelligence
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Àrees temàtiques de la UPC::Informàtica::Robòtica
title_short Exploiting symmetries in reinforcement learning of bimanual robotic tasks
title_full Exploiting symmetries in reinforcement learning of bimanual robotic tasks
title_fullStr Exploiting symmetries in reinforcement learning of bimanual robotic tasks
title_full_unstemmed Exploiting symmetries in reinforcement learning of bimanual robotic tasks
title_sort Exploiting symmetries in reinforcement learning of bimanual robotic tasks
dc.creator.none.fl_str_mv Amadio, Fabio
Colomé Figueras, Adrià
Torras, Carme|||0000-0002-2933-398X
author Amadio, Fabio
author_facet Amadio, Fabio
Colomé Figueras, Adrià
Torras, Carme|||0000-0002-2933-398X
author_role author
author2 Colomé Figueras, Adrià
Torras, Carme|||0000-0002-2933-398X
author2_role author
author
dc.subject.none.fl_str_mv Humanoid robots
Learning
Artificial intelligence
Manipulators
Classificació INSPEC::Cybernetics::Artificial intelligence
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Àrees temàtiques de la UPC::Informàtica::Robòtica
topic Humanoid robots
Learning
Artificial intelligence
Manipulators
Classificació INSPEC::Cybernetics::Artificial intelligence
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Àrees temàtiques de la UPC::Informàtica::Robòtica
description © 2019 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.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-01-01
2020
2020-02-21
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/178327
https://dx.doi.org/10.1109/LRA.2019.2898330
url https://hdl.handle.net/2117/178327
https://dx.doi.org/10.1109/LRA.2019.2898330
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 741930 CLOTH manIpulation Learning from DEmonstrations
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
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 (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300724

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