A framework for learning in humanoid simulated robots

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dc.contributor.authorColombini, Esther Luna
dc.contributor.authorDa Silva Simöes, Alexandre [UNESP]
dc.contributor.authorMartins, Antônio Cesar Germano [UNESP]
dc.contributor.authorMatsuura, Jackson Paul
dc.contributor.institutionInstituto Tecnológico de Aeronáutica (ITA)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-27T11:23:38Z
dc.date.available2014-05-27T11:23:38Z
dc.date.issued2008-09-01
dc.description.abstractOne of the most important characteristics of intelligent activity is the ability to change behaviour according to many forms of feedback. Through learning an agent can interact with its environment to improve its performance over time. However, most of the techniques known that involves learning are time expensive, i.e., once the agent is supposed to learn over time by experimentation, the task has to be executed many times. Hence, high fidelity simulators can save a lot of time. In this context, this paper describes the framework designed to allow a team of real RoboNova-I humanoids robots to be simulated under USARSim environment. Details about the complete process of modeling and programming the robot are given, as well as the learning methodology proposed to improve robot's performance. Due to the use of a high fidelity model, the learning algorithms can be widely explored in simulation before adapted to real robots. © 2008 Springer-Verlag Berlin Heidelberg.en
dc.description.affiliationItandroids Research Group Technological Institute of Aeronautics (ITA)
dc.description.affiliationAutomation and Integrated Systems Group (GASI) São Paulo State University (UNESP)
dc.description.affiliationUnespAutomation and Integrated Systems Group (GASI) São Paulo State University (UNESP)
dc.format.extent345-352
dc.identifierhttp://dx.doi.org/10.1007/978-3-540-68847-1_34
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5001 LNAI, p. 345-352.
dc.identifier.doi10.1007/978-3-540-68847-1_34
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.scopus2-s2.0-50249101157
dc.identifier.urihttp://hdl.handle.net/11449/70539
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofsjr0,295
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectEducation
dc.subjectLearning systems
dc.subjectRobot programming
dc.subjectRobotics
dc.subjectRobots
dc.subjectHigh-fidelity
dc.subjectHigh-fidelity simulators
dc.subjectInternational symposium
dc.subjectReal robots
dc.subjectRoboCup
dc.subjectRobot-soccer
dc.subjectSimulated robots
dc.subjectTo many
dc.subjectWorld Cup
dc.subjectLearning algorithms
dc.titleA framework for learning in humanoid simulated robotsen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.springer.com/open+access/authors+rights
unesp.author.lattes1368002066043197[2]
unesp.author.lattes9625195382275378[3]
unesp.author.orcid0000-0002-1457-6305[2]
unesp.author.orcid0000-0002-7683-8729[3]

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