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Movement Primitive Learning and Generalization : Using Mixture Density Networks

Zhou, Y.; Gao, J.; Asfour, T.

Abstract:
Representing robot skills as movement primitives (MPs) that can be learned from human demonstration and adapted to new tasks and situations is a promising approach toward intuitive robot programming. To allow such adaptation, mapping between task parameters and MP parameters is needed, and different approaches have been proposed in the literature to learn such mapping. In human demonstrations, however, multiple modes and models exist, and these should be taken into account when learning these mappings and generalized MP representations.

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Verlagsausgabe §
DOI: 10.5445/IR/1000118851
Veröffentlicht am 17.07.2020
Originalveröffentlichung
DOI: 10.1109/MRA.2020.2980591
Scopus
Zitationen: 2
Web of Science
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 1070-9932, 1558-223X
KITopen-ID: 1000118851
Erschienen in IEEE robotics & automation magazine
Verlag IEEE Robotics and Automation Society
Band 27
Heft 2
Seiten 22 - 32
Vorab online veröffentlicht am 06.04.2020
Nachgewiesen in Web of Science
Scopus
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