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Reducing the number of evaluations required for CGDA execution through Particle Swarm Optimization methods

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dc.affiliation.dptoUC3M. Departamento de Ingeniería de Sistemas y Automáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Sistemas Inteligenteses
dc.contributor.authorFernández Fernández, Raúl
dc.contributor.authorEstévez Fernández, David
dc.contributor.authorGonzález Víctores, Juan Carlos
dc.contributor.authorBalaguer Bernaldo de Quirós, Carlos
dc.contributor.funderComunidad de Madrides
dc.date.accessioned2022-02-08T12:18:09Z
dc.date.available2022-02-08T12:18:09Z
dc.date.issued2017-04-26
dc.descriptionProceedings of: 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), 26-28 April 2017, Coimbra, Portugal.en
dc.description.abstractContinuous Goal Directed Actions (CGDA) is a robot learning framework that encodes actions as time series of object and environment scalar features. As the execution of actions is not encoded explicitly, robot joint trajectories are computed through Evolutionary Algorithms (EA), which require a large number of evaluations. The consequence is that evaluations are performed in a simulated environment, and the optimal robot trajectory computed is then transferred to the actual robot. This paper focuses on reducing the number of evaluations required for computing an optimal robot joint trajectory. Particle Swarm Optimization (PSO) methods have been adapted to the CGDA framework to be studied and compared: naíve PSO, Adaptive Fuzzy Fitness Granulation PSO (AFFG-PSO), and Fitness Inheritance PSO (FI-PSO). Experiments have been performed for two representative use cases within CGDA: the “wax” and the “painting” action. The experimental results of PSO methods are compared with those obtained with the Steady State Tournament used in the original proposal of CGDA. Conclusions extracted from these results depict a reduction of the number of required evaluations, with simultaneous tradeoff regarding the degree of fulfillment of the objective given by the optimization cost function.en
dc.description.sponsorshipThe research leading to these results has received funding from the RoboCity2030-III-CM project (Robtica aplicada a la mejora de la calidad de vida de los ciudadanos, fase Ill; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU, and by a FPU grant funded by Miniesterio de Educaci6n, Cultura y deporte.en
dc.format.extent6
dc.identifier.bibliographicCitationFernandez-Fernandez, R., Estevez, D., Victores, J. G. & Balaguer, C. (26-28 April 2017). Reducing the number of evaluations required for CGDA execution through Particle Swarm Optimization methods [proceedings]. 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Coimbra, Portugal.en
dc.identifier.doihttps://doi.org/10.1109/ICARSC.2017.7964089
dc.identifier.isbn978-1-5090-6235-5
dc.identifier.publicationfirstpage284
dc.identifier.publicationlastpage289
dc.identifier.publicationtitle2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)en
dc.identifier.urihttps://hdl.handle.net/10016/34067
dc.identifier.uxxiCC/0000029055
dc.language.isoengen
dc.publisherIEEEen
dc.relation.eventdate2017-04-26
dc.relation.eventplacePortugales
dc.relation.eventtitle2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)en
dc.relation.projectIDComunidad de Madrid. S2013/MIT-2748es
dc.rights© 2017, IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaRobótica e Informática Industriales
dc.subject.otherTrajectoryen
dc.subject.otherParticle swarm optimisationen
dc.subject.otherRobotsen
dc.subject.otherTime Seriesen
dc.titleReducing the number of evaluations required for CGDA execution through Particle Swarm Optimization methodsen
dc.typeconference proceedings*
dc.type.hasVersionAM*
dspace.entity.typePublication
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