Social robot navigation tasks: Combining machine learning techniques and social force model

Social robot navigation in public spaces, buildings or private houses is a difficult problem that is not well solved due to environmental constraints (buildings, static objects etc.), pedestrians and other mobile vehicles. Moreover, robots have to move in a human-aware manner—that is, robots have to...

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Detalles Bibliográficos
Autores: Gil Viyuela, Óscar, Garrell, Anaís, Sanfeliu, Alberto
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/261306
Acceso en línea:http://hdl.handle.net/10261/261306
Access Level:acceso abierto
Palabra clave:Social robot navigation
Social force model
Reinforcement learning
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spelling Social robot navigation tasks: Combining machine learning techniques and social force modelGil Viyuela, ÓscarGarrell, AnaísSanfeliu, AlbertoSocial robot navigationSocial force modelReinforcement learningSocial robot navigation in public spaces, buildings or private houses is a difficult problem that is not well solved due to environmental constraints (buildings, static objects etc.), pedestrians and other mobile vehicles. Moreover, robots have to move in a human-aware manner—that is, robots have to navigate in such a way that people feel safe and comfortable. In this work, we present two navigation tasks, social robot navigation and robot accompaniment, which combine machine learning techniques with the Social Force Model (SFM) allowing human-aware social navigation. The robots in both approaches use data from different sensors to capture the environment knowledge as well as information from pedestrian motion. The two navigation tasks make use of the SFM, which is a general framework in which human motion behaviors can be expressed through a set of functions depending on the pedestrians’ relative and absolute positions and velocities. Additionally, in both social navigation tasks, the robot’s motion behavior is learned using machine learning techniques: in the first case using supervised deep learning techniques and, in the second case, using Reinforcement Learning (RL). The machine learning techniques are combined with the SFM to create navigation models that behave in a social manner when the robot is navigating in an environment with pedestrians or accompanying a person. The validation of the systems was performed with a large set of simulations and real-life experiments with a new humanoid robot denominated IVO and with an aerial robot. The experiments show that the combination of SFM and machine learning can solve human-aware robot navigation in complex dynamic environments.This research was supported by the grant MDM-2016-0656 funded by MCIN/AEI/ 10.13039/501100011033, the grant ROCOTRANSP PID2019-106702RB-C21 funded by MCIN/AEI/ 10.13039/501100011033 and the grant CANOPIES H2020-ICT-2020-2-101016906 funded by the European Union.Molecular Diversity Preservation InternationalMinisterio de Economía y Competitividad (España)Ministerio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)European CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2022202220212022info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/261306reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MINECO//MDM-2016-0656info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-106702RB-C21info:eu-repo/grantAgreement/EC/H2020/101016906http://dx.doi.org/10.3390/s21217087Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2613062026-05-22T06:33:51Z
dc.title.none.fl_str_mv Social robot navigation tasks: Combining machine learning techniques and social force model
title Social robot navigation tasks: Combining machine learning techniques and social force model
spellingShingle Social robot navigation tasks: Combining machine learning techniques and social force model
Gil Viyuela, Óscar
Social robot navigation
Social force model
Reinforcement learning
title_short Social robot navigation tasks: Combining machine learning techniques and social force model
title_full Social robot navigation tasks: Combining machine learning techniques and social force model
title_fullStr Social robot navigation tasks: Combining machine learning techniques and social force model
title_full_unstemmed Social robot navigation tasks: Combining machine learning techniques and social force model
title_sort Social robot navigation tasks: Combining machine learning techniques and social force model
dc.creator.none.fl_str_mv Gil Viyuela, Óscar
Garrell, Anaís
Sanfeliu, Alberto
author Gil Viyuela, Óscar
author_facet Gil Viyuela, Óscar
Garrell, Anaís
Sanfeliu, Alberto
author_role author
author2 Garrell, Anaís
Sanfeliu, Alberto
author2_role author
author
dc.contributor.none.fl_str_mv Ministerio de Economía y Competitividad (España)
Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Social robot navigation
Social force model
Reinforcement learning
topic Social robot navigation
Social force model
Reinforcement learning
description Social robot navigation in public spaces, buildings or private houses is a difficult problem that is not well solved due to environmental constraints (buildings, static objects etc.), pedestrians and other mobile vehicles. Moreover, robots have to move in a human-aware manner—that is, robots have to navigate in such a way that people feel safe and comfortable. In this work, we present two navigation tasks, social robot navigation and robot accompaniment, which combine machine learning techniques with the Social Force Model (SFM) allowing human-aware social navigation. The robots in both approaches use data from different sensors to capture the environment knowledge as well as information from pedestrian motion. The two navigation tasks make use of the SFM, which is a general framework in which human motion behaviors can be expressed through a set of functions depending on the pedestrians’ relative and absolute positions and velocities. Additionally, in both social navigation tasks, the robot’s motion behavior is learned using machine learning techniques: in the first case using supervised deep learning techniques and, in the second case, using Reinforcement Learning (RL). The machine learning techniques are combined with the SFM to create navigation models that behave in a social manner when the robot is navigating in an environment with pedestrians or accompanying a person. The validation of the systems was performed with a large set of simulations and real-life experiments with a new humanoid robot denominated IVO and with an aerial robot. The experiments show that the combination of SFM and machine learning can solve human-aware robot navigation in complex dynamic environments.
publishDate 2021
dc.date.none.fl_str_mv 2021
2022
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/261306
url http://hdl.handle.net/10261/261306
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/MINECO//MDM-2016-0656
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-106702RB-C21
info:eu-repo/grantAgreement/EC/H2020/101016906
http://dx.doi.org/10.3390/s21217087
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Molecular Diversity Preservation International
publisher.none.fl_str_mv Molecular Diversity Preservation International
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
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collection DIGITAL.CSIC. Repositorio Institucional del CSIC
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