A neural integrator model for planning and value-based decision making of a robotics assistant
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/69412 |
Resumo: | Modern manufacturing and assembly environments are characterized by a high variability in the built process which challenges human–robot cooperation. To reduce the cognitive workload of the operator, the robot should not only be able to learn from experience but also to plan and decide autonomously. Here, we present an approach based on Dynamic Neural Fields that apply brain-like computations to endow a robot with these cognitive functions. A neural integrator is used to model the gradual accumulation of sensory and other evidence as time-varying persistent activity of neural populations. The decision to act is modeled by a competitive dynamics between neural populations linked to different motor behaviors. They receive the persistent activation pattern of the integrators as input. In the first experiment, a robot learns rapidly by observation the sequential order of object transfers between an assistant and an operator to subsequently substitute the assistant in the joint task. The results show that the robot is able to proactively plan the series of handovers in the correct order. In the second experiment, a mobile robot searches at two different workbenches for a specific object to deliver it to an operator. The object may appear at the two locations in a certain time period with independent probabilities unknown to the robot. The trial-by-trial decision under uncertainty is biased by the accumulated evidence of past successes and choices. The choice behavior over a longer period reveals that the robot achieves a high search efficiency in stationary as well as dynamic environments. |
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A neural integrator model for planning and value-based decision making of a robotics assistantValue-based decision makingAction planningDynamic field theorySevice roboticsLearningDynamic Neural FieldNeural integratorAssembly robotSequence learningCiências Naturais::MatemáticasScience & TechnologyModern manufacturing and assembly environments are characterized by a high variability in the built process which challenges human–robot cooperation. To reduce the cognitive workload of the operator, the robot should not only be able to learn from experience but also to plan and decide autonomously. Here, we present an approach based on Dynamic Neural Fields that apply brain-like computations to endow a robot with these cognitive functions. A neural integrator is used to model the gradual accumulation of sensory and other evidence as time-varying persistent activity of neural populations. The decision to act is modeled by a competitive dynamics between neural populations linked to different motor behaviors. They receive the persistent activation pattern of the integrators as input. In the first experiment, a robot learns rapidly by observation the sequential order of object transfers between an assistant and an operator to subsequently substitute the assistant in the joint task. The results show that the robot is able to proactively plan the series of handovers in the correct order. In the second experiment, a mobile robot searches at two different workbenches for a specific object to deliver it to an operator. The object may appear at the two locations in a certain time period with independent probabilities unknown to the robot. The trial-by-trial decision under uncertainty is biased by the accumulated evidence of past successes and choices. The choice behavior over a longer period reveals that the robot achieves a high search efficiency in stationary as well as dynamic environments.The work received financial support from FCT through the PhD fellowships PD/BD/128183/2016 and SFRH/BD/124912/2016, the project “Neurofield” (PTDC/MAT-APL/31393/2017) and the research centre CMAT within the project UID/MAT/00013/2013.Springer NatureUniversidade do MinhoWojtak, WeronikaFerreira, Flora José RochaVicente, Paulo Sérgio CunhaLouro, LuísBicho, EstelaErlhagen, Wolfram20202020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/69412eng0941-064310.1007/s00521-020-05224-8http://link.springer.com/article/10.1007/s00521-020-05224-8info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-23T01:31:02Zoai:repositorium.sdum.uminho.pt:1822/69412Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:17:06.596103Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A neural integrator model for planning and value-based decision making of a robotics assistant |
title |
A neural integrator model for planning and value-based decision making of a robotics assistant |
spellingShingle |
A neural integrator model for planning and value-based decision making of a robotics assistant Wojtak, Weronika Value-based decision making Action planning Dynamic field theory Sevice robotics Learning Dynamic Neural Field Neural integrator Assembly robot Sequence learning Ciências Naturais::Matemáticas Science & Technology |
title_short |
A neural integrator model for planning and value-based decision making of a robotics assistant |
title_full |
A neural integrator model for planning and value-based decision making of a robotics assistant |
title_fullStr |
A neural integrator model for planning and value-based decision making of a robotics assistant |
title_full_unstemmed |
A neural integrator model for planning and value-based decision making of a robotics assistant |
title_sort |
A neural integrator model for planning and value-based decision making of a robotics assistant |
author |
Wojtak, Weronika |
author_facet |
Wojtak, Weronika Ferreira, Flora José Rocha Vicente, Paulo Sérgio Cunha Louro, Luís Bicho, Estela Erlhagen, Wolfram |
author_role |
author |
author2 |
Ferreira, Flora José Rocha Vicente, Paulo Sérgio Cunha Louro, Luís Bicho, Estela Erlhagen, Wolfram |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Wojtak, Weronika Ferreira, Flora José Rocha Vicente, Paulo Sérgio Cunha Louro, Luís Bicho, Estela Erlhagen, Wolfram |
dc.subject.por.fl_str_mv |
Value-based decision making Action planning Dynamic field theory Sevice robotics Learning Dynamic Neural Field Neural integrator Assembly robot Sequence learning Ciências Naturais::Matemáticas Science & Technology |
topic |
Value-based decision making Action planning Dynamic field theory Sevice robotics Learning Dynamic Neural Field Neural integrator Assembly robot Sequence learning Ciências Naturais::Matemáticas Science & Technology |
description |
Modern manufacturing and assembly environments are characterized by a high variability in the built process which challenges human–robot cooperation. To reduce the cognitive workload of the operator, the robot should not only be able to learn from experience but also to plan and decide autonomously. Here, we present an approach based on Dynamic Neural Fields that apply brain-like computations to endow a robot with these cognitive functions. A neural integrator is used to model the gradual accumulation of sensory and other evidence as time-varying persistent activity of neural populations. The decision to act is modeled by a competitive dynamics between neural populations linked to different motor behaviors. They receive the persistent activation pattern of the integrators as input. In the first experiment, a robot learns rapidly by observation the sequential order of object transfers between an assistant and an operator to subsequently substitute the assistant in the joint task. The results show that the robot is able to proactively plan the series of handovers in the correct order. In the second experiment, a mobile robot searches at two different workbenches for a specific object to deliver it to an operator. The object may appear at the two locations in a certain time period with independent probabilities unknown to the robot. The trial-by-trial decision under uncertainty is biased by the accumulated evidence of past successes and choices. The choice behavior over a longer period reveals that the robot achieves a high search efficiency in stationary as well as dynamic environments. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 2020-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/69412 |
url |
https://hdl.handle.net/1822/69412 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0941-0643 10.1007/s00521-020-05224-8 http://link.springer.com/article/10.1007/s00521-020-05224-8 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer Nature |
publisher.none.fl_str_mv |
Springer Nature |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799132622776958976 |