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Robot imitation through vision, kinesthetic and force features with online adaptation to changing environments

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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.authorGonzález Víctores, Juan Carlos
dc.contributor.authorEstévez Fernández, David
dc.contributor.authorBalaguer Bernaldo de Quirós, Carlos
dc.contributor.funderComunidad de Madrides
dc.date.accessioned2022-02-08T12:47:05Z
dc.date.available2022-02-08T12:47:05Z
dc.date.issued2018-10-01
dc.descriptionProceedings of: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1-5 October 2018, Madrid, Spain.en
dc.description.abstractContinuous Goal-Directed Actions (CGDA)is a robot imitation framework that encodes actions as the changes they produce on the environment. While it presents numerous advantages with respect to other robot imitation frameworks in terms of generalization and portability, final robot joint trajectories for the execution of actions are not necessarily encoded within the model. This is studied as an optimization problem, and the solution is computed through evolutionary algorithms in simulated environments. Evolutionary algorithms require a large number of evaluations, which had made the use of these algorithms in real world applications very challenging. This paper presents online evolutionary strategies, as a change of paradigm within CGDA execution. Online evolutionary strategies shift and merge motor execution into the planning loop. A concrete online evolutionary strategy, Online Evolved Trajectories (OET), is presented. OET drastically reduces computational times between motor executions, and enables working in real world dynamic environments and/or with human collaboration. Its performance has been measured against Full Trajectory Evolution (FTE)and Incrementally Evolved Trajectories (IET), obtaining the best overall results. Experimental evaluations are performed on the TEO full-sized humanoid robot with “paint” and “iron” actions that together involve vision, kinesthetic and force features.en
dc.description.sponsorshipThe research leading to these results has received funding from the RoboCity2030-III-CM project (Robotica aplicada a la mejora de la calidad de vida de los ciudadanos. Fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU.en
dc.format.extent6
dc.identifier.bibliographicCitationFernandez-Fernandez, R., Victores, J. G., Estevez, D. & Balaguer, C. (1-5 October 2018). Robot Imitation Through Vision, Kinesthetic and Force Features with Online Adaptation to Changing Environments [proceedings]. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.en
dc.identifier.doihttps://doi.org/10.1109/IROS.2018.8593724
dc.identifier.isbn978-1-5386-8094-0
dc.identifier.publicationfirstpage6546
dc.identifier.publicationlastpage6551
dc.identifier.publicationtitle2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)en
dc.identifier.urihttps://hdl.handle.net/10016/34069
dc.identifier.uxxiCC/0000029442
dc.language.isoengen
dc.publisherIEEEen
dc.relation.eventdate2018-10-01
dc.relation.eventplaceMadrides
dc.relation.eventtitle2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)en
dc.relation.projectIDComunidad de Madrid. S2013/MIT-2748es
dc.rights© 2018, IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaRobótica e Informática Industriales
dc.subject.otherTrajectoryen
dc.subject.otherRobot sensing systemsen
dc.subject.otherFeature extractionen
dc.subject.otherForceen
dc.subject.otherPlanningen
dc.subject.otherPaintsen
dc.titleRobot imitation through vision, kinesthetic and force features with online adaptation to changing environmentsen
dc.typeconference proceedings*
dc.type.hasVersionAM*
dspace.entity.typePublication
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