Evolution of hybrid robotic controllers for complex tasks

Detalhes bibliográficos
Autor(a) principal: Duarte, M.
Data de Publicação: 2015
Outros Autores: Oliveira, S. M., Christensen, A. L.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10071/9347
Resumo: We propose an approach to the synthesis of hierarchical control systems comprising both evolved and manually programmed control for autonomous robots. We recursively divide the goal task into sub-tasks until a solution can be evolved or until a solution can easily be programmed by hand. Hierarchical composition of behavior allows us to overcome the fundamental challenges that typically prevent evolutionary robotics from being applied to complex tasks: bootstrapping the evolutionary process, avoiding deception, and successfully transferring control evolved in simulation to real robotic hardware. We demonstrate the proposed approach by synthesizing control systems for two tasks whose complexity is beyond state of the art in evolutionary robotics. The first task is a rescue task in which all behaviors are evolved. The second task is a cleaning task in which evolved behaviors are combined with a manually programmed behavior that enables the robot to open doors in the environment. We demonstrate incremental transfer of evolved control from simulation to real robotic hardware, and we show how our approach allows for the reuse of behaviors in different tasks.
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spelling Evolution of hybrid robotic controllers for complex tasksEvolutionary roboticsHierarchical controlArtificial evolutionWe propose an approach to the synthesis of hierarchical control systems comprising both evolved and manually programmed control for autonomous robots. We recursively divide the goal task into sub-tasks until a solution can be evolved or until a solution can easily be programmed by hand. Hierarchical composition of behavior allows us to overcome the fundamental challenges that typically prevent evolutionary robotics from being applied to complex tasks: bootstrapping the evolutionary process, avoiding deception, and successfully transferring control evolved in simulation to real robotic hardware. We demonstrate the proposed approach by synthesizing control systems for two tasks whose complexity is beyond state of the art in evolutionary robotics. The first task is a rescue task in which all behaviors are evolved. The second task is a cleaning task in which evolved behaviors are combined with a manually programmed behavior that enables the robot to open doors in the environment. We demonstrate incremental transfer of evolved control from simulation to real robotic hardware, and we show how our approach allows for the reuse of behaviors in different tasks.Springer2015-07-17T18:02:53Z2015-01-01T00:00:00Z20152019-03-26T17:23:38Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/9347eng0921-029610.1007/s10846-014-0086-xDuarte, M.Oliveira, S. M.Christensen, A. L.info: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:RCAAP2023-11-09T17:53:54Zoai:repositorio.iscte-iul.pt:10071/9347Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:27:05.122033Repositório 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 Evolution of hybrid robotic controllers for complex tasks
title Evolution of hybrid robotic controllers for complex tasks
spellingShingle Evolution of hybrid robotic controllers for complex tasks
Duarte, M.
Evolutionary robotics
Hierarchical control
Artificial evolution
title_short Evolution of hybrid robotic controllers for complex tasks
title_full Evolution of hybrid robotic controllers for complex tasks
title_fullStr Evolution of hybrid robotic controllers for complex tasks
title_full_unstemmed Evolution of hybrid robotic controllers for complex tasks
title_sort Evolution of hybrid robotic controllers for complex tasks
author Duarte, M.
author_facet Duarte, M.
Oliveira, S. M.
Christensen, A. L.
author_role author
author2 Oliveira, S. M.
Christensen, A. L.
author2_role author
author
dc.contributor.author.fl_str_mv Duarte, M.
Oliveira, S. M.
Christensen, A. L.
dc.subject.por.fl_str_mv Evolutionary robotics
Hierarchical control
Artificial evolution
topic Evolutionary robotics
Hierarchical control
Artificial evolution
description We propose an approach to the synthesis of hierarchical control systems comprising both evolved and manually programmed control for autonomous robots. We recursively divide the goal task into sub-tasks until a solution can be evolved or until a solution can easily be programmed by hand. Hierarchical composition of behavior allows us to overcome the fundamental challenges that typically prevent evolutionary robotics from being applied to complex tasks: bootstrapping the evolutionary process, avoiding deception, and successfully transferring control evolved in simulation to real robotic hardware. We demonstrate the proposed approach by synthesizing control systems for two tasks whose complexity is beyond state of the art in evolutionary robotics. The first task is a rescue task in which all behaviors are evolved. The second task is a cleaning task in which evolved behaviors are combined with a manually programmed behavior that enables the robot to open doors in the environment. We demonstrate incremental transfer of evolved control from simulation to real robotic hardware, and we show how our approach allows for the reuse of behaviors in different tasks.
publishDate 2015
dc.date.none.fl_str_mv 2015-07-17T18:02:53Z
2015-01-01T00:00:00Z
2015
2019-03-26T17:23:38Z
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url http://hdl.handle.net/10071/9347
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0921-0296
10.1007/s10846-014-0086-x
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publisher.none.fl_str_mv Springer
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