Task-adaptive robot learning from demonstration with gaussian process models under replication

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Authors: Arduengo García, Miguel, Colomé Figueras, Adrià, Borràs Sol, Júlia, Sentís Álvarez, Luis, Torras, Carme|||0000-0002-2933-398X
Format: article
Publication Date:2021
Country:España
Institution:Universitat Politècnica de Catalunya (UPC)
Repository:UPCommons. Portal del coneixement obert de la UPC
Language:English
OAI Identifier:oai:upcommons.upc.edu:2117/358399
Online Access:https://hdl.handle.net/2117/358399
https://dx.doi.org/10.1109/LRA.2021.3056367
Access Level:Open access
Keyword:Humanoid robots
Intelligent robots
Learning (artificial intelligence)
Classificació INSPEC::Cybernetics::Artificial intelligence
Àrees temàtiques de la UPC::Informàtica::Robòtica
id ES_d82f4f6c856cfb72903825608eff7bf1
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spelling Task-adaptive robot learning from demonstration with gaussian process models under replicationArduengo García, MiguelColomé Figueras, AdriàBorràs Sol, JúliaSentís Álvarez, LuisTorras, Carme|||0000-0002-2933-398XHumanoid robotsIntelligent robotsLearning (artificial intelligence)Classificació INSPEC::Cybernetics::Artificial intelligenceÀrees temàtiques de la UPC::Informàtica::Robòtica© 2021 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.Learning from Demonstration (LfD) is a paradigm that allows robots to learn complex manipulation tasks that can not be easily scripted, but can be demonstrated by a human teacher. One of the challenges of LfD is to enable robots to acquire skills that can be adapted to different scenarios. In this paper, we propose to achieve this by exploiting the variations in the demonstrations to retrieve an adaptive and robust policy, using Gaussian Process (GP) models. Adaptability is enhanced by incorporating task parameters into the model, which encode different specifications within the same task. With our formulation, these parameters can be either real, integer, or categorical. Furthermore, we propose a GP design that exploits the structure of replications, i.e., repeated demonstrations with identical conditions within data. Our method significantly reduces the computational cost of model fitting in complex tasks, where replications are essential to obtain a robust model. We illustrate our approach through several experiments on a handwritten letter demonstration dataset.This work has been partially funded by the European Union Horizon 2020 Programme under grant agreement no. 741930 (CLOTHILDE) and by the Spanish State Research Agency through the Mar ́ıa de Maeztu Seal of Excellence to IRI [MDM-2016-0656].Peer ReviewedInstitute of Electrical and Electronics Engineers (IEEE)20212021-01-0120212021-12-14journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/358399https://dx.doi.org/10.1109/LRA.2021.3056367reponame: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:2117/3583992026-05-27T15:37:01Z
dc.title.none.fl_str_mv Task-adaptive robot learning from demonstration with gaussian process models under replication
title Task-adaptive robot learning from demonstration with gaussian process models under replication
spellingShingle Task-adaptive robot learning from demonstration with gaussian process models under replication
Arduengo García, Miguel
Humanoid robots
Intelligent robots
Learning (artificial intelligence)
Classificació INSPEC::Cybernetics::Artificial intelligence
Àrees temàtiques de la UPC::Informàtica::Robòtica
title_short Task-adaptive robot learning from demonstration with gaussian process models under replication
title_full Task-adaptive robot learning from demonstration with gaussian process models under replication
title_fullStr Task-adaptive robot learning from demonstration with gaussian process models under replication
title_full_unstemmed Task-adaptive robot learning from demonstration with gaussian process models under replication
title_sort Task-adaptive robot learning from demonstration with gaussian process models under replication
dc.creator.none.fl_str_mv Arduengo García, Miguel
Colomé Figueras, Adrià
Borràs Sol, Júlia
Sentís Álvarez, Luis
Torras, Carme|||0000-0002-2933-398X
author Arduengo García, Miguel
author_facet Arduengo García, Miguel
Colomé Figueras, Adrià
Borràs Sol, Júlia
Sentís Álvarez, Luis
Torras, Carme|||0000-0002-2933-398X
author_role author
author2 Colomé Figueras, Adrià
Borràs Sol, Júlia
Sentís Álvarez, Luis
Torras, Carme|||0000-0002-2933-398X
author2_role author
author
author
author
dc.subject.none.fl_str_mv Humanoid robots
Intelligent robots
Learning (artificial intelligence)
Classificació INSPEC::Cybernetics::Artificial intelligence
Àrees temàtiques de la UPC::Informàtica::Robòtica
topic Humanoid robots
Intelligent robots
Learning (artificial intelligence)
Classificació INSPEC::Cybernetics::Artificial intelligence
Àrees temàtiques de la UPC::Informàtica::Robòtica
description © 2021 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 2021
dc.date.none.fl_str_mv 2021
2021-01-01
2021
2021-12-14
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/2117/358399
https://dx.doi.org/10.1109/LRA.2021.3056367
url https://hdl.handle.net/2117/358399
https://dx.doi.org/10.1109/LRA.2021.3056367
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 15,301603

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