Robust and adaptive door operation with a mobile robot

The ability to deal with articulated objects is very important for robots assisting humans. In this work, a framework to robustly and adaptively operate common doors, using an autonomous mobile manipulator, is proposed. To push forward the state of the art in robustness and speed performance, we dev...

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Detalhes bibliográficos
Autores: Arduengo, Miguel, Torras, Carme, Sentis, Luis
Tipo de documento: artigo
Estado:Versión aceptada para publicación
Data de publicação:2021
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositório:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/261269
Acesso em linha:http://hdl.handle.net/10261/261269
Access Level:Acceso aberto
Palavra-chave:Handle grasping
Door operation
Kinematic model learning
Task space region
Service robot
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spelling Robust and adaptive door operation with a mobile robotArduengo, MiguelTorras, CarmeSentis, LuisHandle graspingDoor operationKinematic model learningTask space regionService robotThe ability to deal with articulated objects is very important for robots assisting humans. In this work, a framework to robustly and adaptively operate common doors, using an autonomous mobile manipulator, is proposed. To push forward the state of the art in robustness and speed performance, we devise a novel algorithm that fuses a convolutional neural network with efficient point cloud processing. This advancement enables real-time grasping pose estimation for multiple handles from RGB-D images, providing a speed up improvement for assistive human-centered applications. In addition, we propose a versatile Bayesian framework that endows the robot with the ability to infer the door kinematic model from observations of its motion and learn from previous experiences or human demonstrations. Combining these algorithms with a Task Space Region motion planner, we achieve an efficient door operation regardless of the kinematic model. We validate our framework with real-world experiments using the Toyota human support robot.Springer NatureConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2022202220212022info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/261269reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.1007/s11370-021-00366-7Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2612692026-05-22T06:33:51Z
dc.title.none.fl_str_mv Robust and adaptive door operation with a mobile robot
title Robust and adaptive door operation with a mobile robot
spellingShingle Robust and adaptive door operation with a mobile robot
Arduengo, Miguel
Handle grasping
Door operation
Kinematic model learning
Task space region
Service robot
title_short Robust and adaptive door operation with a mobile robot
title_full Robust and adaptive door operation with a mobile robot
title_fullStr Robust and adaptive door operation with a mobile robot
title_full_unstemmed Robust and adaptive door operation with a mobile robot
title_sort Robust and adaptive door operation with a mobile robot
dc.creator.none.fl_str_mv Arduengo, Miguel
Torras, Carme
Sentis, Luis
author Arduengo, Miguel
author_facet Arduengo, Miguel
Torras, Carme
Sentis, Luis
author_role author
author2 Torras, Carme
Sentis, Luis
author2_role author
author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Handle grasping
Door operation
Kinematic model learning
Task space region
Service robot
topic Handle grasping
Door operation
Kinematic model learning
Task space region
Service robot
description The ability to deal with articulated objects is very important for robots assisting humans. In this work, a framework to robustly and adaptively operate common doors, using an autonomous mobile manipulator, is proposed. To push forward the state of the art in robustness and speed performance, we devise a novel algorithm that fuses a convolutional neural network with efficient point cloud processing. This advancement enables real-time grasping pose estimation for multiple handles from RGB-D images, providing a speed up improvement for assistive human-centered applications. In addition, we propose a versatile Bayesian framework that endows the robot with the ability to infer the door kinematic model from observations of its motion and learn from previous experiences or human demonstrations. Combining these algorithms with a Task Space Region motion planner, we achieve an efficient door operation regardless of the kinematic model. We validate our framework with real-world experiments using the Toyota human support robot.
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
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/261269
url http://hdl.handle.net/10261/261269
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.1007/s11370-021-00366-7
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
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)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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