Goal-directed Imitation for Robots: a bio-inspired approach to action understanding and skill learning
Autor(a) principal: | |
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Data de Publicação: | 2006 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
Texto Completo: | https://hdl.handle.net/1822/5938 |
Resumo: | In this paper we present a robot control architecture for learning by imitation which takes inspiration from recent discoveries in action observation/execution experiments with humans and other primates. The architecture implements two basic processing principles: 1) imitation is primarily directed toward reproducing the goal/end state of an observed action sequence, and 2) the required capacity to understand the motor intention of another agent is based on motor simulation. The control architecture is validated in a robot system imitating in a goal-directed manner a grasping and placing sequence displayed by a human model. During imitation, skill transfer occurs by learning and representing ppropriate goal-directed sequences of motor primitives. After having established computational links between the representations of goal and means, further knowledge about the meaning of objects is transferred (“where to place specific objects”). The robustness of the goal-directed organization of the controller is tested in the presence of incomplete visual information and changes in environmental constraints. |
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Goal-directed Imitation for Robots: a bio-inspired approach to action understanding and skill learningImitation learningGoal inferenceAction understandingaction sequencedynamic fieldmirror neuronsScience & TechnologyIn this paper we present a robot control architecture for learning by imitation which takes inspiration from recent discoveries in action observation/execution experiments with humans and other primates. The architecture implements two basic processing principles: 1) imitation is primarily directed toward reproducing the goal/end state of an observed action sequence, and 2) the required capacity to understand the motor intention of another agent is based on motor simulation. The control architecture is validated in a robot system imitating in a goal-directed manner a grasping and placing sequence displayed by a human model. During imitation, skill transfer occurs by learning and representing ppropriate goal-directed sequences of motor primitives. After having established computational links between the representations of goal and means, further knowledge about the meaning of objects is transferred (“where to place specific objects”). The robustness of the goal-directed organization of the controller is tested in the presence of incomplete visual information and changes in environmental constraints.European grant ArteSImit (IST-2000-29686)ElsevierUniversidade do MinhoErlhagen, WolframMukovskiy, AlbertBicho, E.Panin, GiorgioKiss, CasbaKnoll, AloisVan Schie, HeinBekkering, Harold2006-052006-05-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/5938eng"Robotics and autonomous systems". ISSN 0921-8890. 54:5 (May 2006) 353-360.0921-889010.1016/j.robot.2006.01.004info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-04-12T04:28:34Zoai:repositorium.sdum.uminho.pt:1822/5938Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T15:13:20.490577Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
dc.title.none.fl_str_mv |
Goal-directed Imitation for Robots: a bio-inspired approach to action understanding and skill learning |
title |
Goal-directed Imitation for Robots: a bio-inspired approach to action understanding and skill learning |
spellingShingle |
Goal-directed Imitation for Robots: a bio-inspired approach to action understanding and skill learning Erlhagen, Wolfram Imitation learning Goal inference Action understanding action sequence dynamic field mirror neurons Science & Technology |
title_short |
Goal-directed Imitation for Robots: a bio-inspired approach to action understanding and skill learning |
title_full |
Goal-directed Imitation for Robots: a bio-inspired approach to action understanding and skill learning |
title_fullStr |
Goal-directed Imitation for Robots: a bio-inspired approach to action understanding and skill learning |
title_full_unstemmed |
Goal-directed Imitation for Robots: a bio-inspired approach to action understanding and skill learning |
title_sort |
Goal-directed Imitation for Robots: a bio-inspired approach to action understanding and skill learning |
author |
Erlhagen, Wolfram |
author_facet |
Erlhagen, Wolfram Mukovskiy, Albert Bicho, E. Panin, Giorgio Kiss, Casba Knoll, Alois Van Schie, Hein Bekkering, Harold |
author_role |
author |
author2 |
Mukovskiy, Albert Bicho, E. Panin, Giorgio Kiss, Casba Knoll, Alois Van Schie, Hein Bekkering, Harold |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Erlhagen, Wolfram Mukovskiy, Albert Bicho, E. Panin, Giorgio Kiss, Casba Knoll, Alois Van Schie, Hein Bekkering, Harold |
dc.subject.por.fl_str_mv |
Imitation learning Goal inference Action understanding action sequence dynamic field mirror neurons Science & Technology |
topic |
Imitation learning Goal inference Action understanding action sequence dynamic field mirror neurons Science & Technology |
description |
In this paper we present a robot control architecture for learning by imitation which takes inspiration from recent discoveries in action observation/execution experiments with humans and other primates. The architecture implements two basic processing principles: 1) imitation is primarily directed toward reproducing the goal/end state of an observed action sequence, and 2) the required capacity to understand the motor intention of another agent is based on motor simulation. The control architecture is validated in a robot system imitating in a goal-directed manner a grasping and placing sequence displayed by a human model. During imitation, skill transfer occurs by learning and representing ppropriate goal-directed sequences of motor primitives. After having established computational links between the representations of goal and means, further knowledge about the meaning of objects is transferred (“where to place specific objects”). The robustness of the goal-directed organization of the controller is tested in the presence of incomplete visual information and changes in environmental constraints. |
publishDate |
2006 |
dc.date.none.fl_str_mv |
2006-05 2006-05-01T00:00:00Z |
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/1822/5938 |
url |
https://hdl.handle.net/1822/5938 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
"Robotics and autonomous systems". ISSN 0921-8890. 54:5 (May 2006) 353-360. 0921-8890 10.1016/j.robot.2006.01.004 |
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 |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
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