Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/54497
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dc.contributor.authorKulvičius, Tomas-
dc.contributor.authorBiehl, Martin-
dc.contributor.authorJavad Aein, Mohamad-
dc.contributor.authorTamošiūnaitė, Minija-
dc.contributor.authorWörgötter, Florentin-
dc.coverage.spatialNL-
dc.date.accessioned2018-10-07T00:13:36Z-
dc.date.available2018-10-07T00:13:36Z-
dc.date.issued2013-
dc.identifier.issn09218890-
dc.identifier.otherVDU02-000014527-
dc.identifier.urihttps://doi.org/10.1016/j.robot.2013.07.009-
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0921889013001358-
dc.description.abstractSince several years dynamic movement primitives (DMPs) are more and more getting into the center of interest for flexible movement control in robotics. In this study we introduce sensory feedback together with a predictive learning mechanism which allows tightly coupled dual-agent systems to learn an adaptive, sensor-driven interaction based on DMPs. The coupled conventional (no-sensors, no learning) DMP-system automatically equilibrates and can still be solved analytically allowing us to derive conditions for stability. When adding adaptive sensor control we can show that both agents learn to cooperate. Simulations as well as real-robot experiments are shown. Interestingly, all these mechanisms are entirely based on low level interactions without any planning or cognitive componenten
dc.description.sponsorshipSistemų analizės katedra-
dc.description.sponsorshipVytauto Didžiojo universitetas-
dc.format.extentp. 1450-1459-
dc.language.isoen-
dc.relation.ispartofRobotics and autonomous systems. Amsterdam : Elsevier Science, Vol. 61, iss. 12, 2013-
dc.relation.isreferencedbyScienceDirect-
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)-
dc.relation.isreferencedbyScopus-
dc.subjectSąveikaujantys agentailt
dc.subjectKliūčių vengimaslt
dc.subjectHebinis mokymasislt
dc.subjectInteractive agentsen
dc.subjectObstacle avoidanceen
dc.subjectHebbian learningen
dc.subject.classificationStraipsnis Clarivate Analytics Web of Science / Article in Clarivate Analytics Web of Science (S1)-
dc.subject.otherInformatika / Informatics (N009)-
dc.titleInteraction learning for dynamic movement primitives used in cooperative robotic tasksen
dc.typeresearch article-
dc.identifier.doihttps://doi.org/10.1016/j.robot.2013.07.009-
dc.identifier.isiWOS:000328178200016-
dcterms.bibliographicCitation45-
dc.date.updated2020-03-09T11:19Z-
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item.fulltextWith Fulltext-
item.grantfulltextopen-
crisitem.author.deptInformatikos fakultetas-
crisitem.author.deptTaikomosios informatikos katedra-
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications
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