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

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dc.contributor.author Fernández Fernández, Raúl
dc.contributor.author González Víctores, Juan Carlos
dc.contributor.author Estévez Fernández, David
dc.contributor.author Balaguer Bernaldo de Quirós, Carlos
dc.date.accessioned 2022-02-08T12:47:05Z
dc.date.available 2022-02-08T12:47:05Z
dc.date.issued 2018-10-01
dc.identifier.bibliographicCitation Fernandez-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.
dc.identifier.isbn 978-1-5386-8094-0
dc.identifier.uri http://hdl.handle.net/10016/34069
dc.description Proceedings of: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1-5 October 2018, Madrid, Spain.
dc.description.abstract Continuous 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.
dc.description.sponsorship The 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.
dc.format.extent 6
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2018, IEEE.
dc.subject.other Trajectory
dc.subject.other Robot sensing systems
dc.subject.other Feature extraction
dc.subject.other Force
dc.subject.other Planning
dc.subject.other Paints
dc.title Robot imitation through vision, kinesthetic and force features with online adaptation to changing environments
dc.type conferenceObject
dc.subject.eciencia Robótica e Informática Industrial
dc.identifier.doi https://doi.org/10.1109/IROS.2018.8593724
dc.rights.accessRights openAccess
dc.relation.projectID Comunidad de Madrid. S2013/MIT-2748
dc.type.version acceptedVersion
dc.relation.eventdate 2018-10-01
dc.relation.eventplace Madrid
dc.relation.eventtitle 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 6546
dc.identifier.publicationlastpage 6551
dc.identifier.publicationtitle 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
dc.identifier.uxxi CC/0000029442
dc.contributor.funder Comunidad de Madrid
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