Learning bowing gesture with motion diversity by dynamic movement primitives

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dc.contributor.authorLim, Chan Soonko
dc.contributor.authorKwon, Dong-Sooko
dc.date.accessioned2018-01-30T02:17:28Z-
dc.date.available2018-01-30T02:17:28Z-
dc.date.created2017-12-29-
dc.date.created2017-12-29-
dc.date.issued2017-07-25-
dc.identifier.citation14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp.165 - 166-
dc.identifier.issn2325-033X-
dc.identifier.urihttp://hdl.handle.net/10203/237977-
dc.description.abstractMotion diversity is an important factor in resembling motion with human-likeness. In this paper, we propose a method by which a robot acquires gesturing skills with motion diversity. We measured the bowing gesture with a 3D capturing camera and used dynamic movement primitives (DMPs) in order to capture the distribution of kernel parameters. The results showed that the height of kernel parameters have a Gaussian distribution and that the trajectory regenerated with the calculated parameters had the desired motion diversity.-
dc.languageEnglish-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleLearning bowing gesture with motion diversity by dynamic movement primitives-
dc.typeConference-
dc.identifier.wosid000426976900036-
dc.identifier.scopusid2-s2.0-85034210469-
dc.type.rimsCONF-
dc.citation.beginningpage165-
dc.citation.endingpage166-
dc.citation.publicationname14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)-
dc.identifier.conferencecountryKO-
dc.identifier.conferencelocationMaison Glad Jeju-
dc.identifier.doi10.1109/URAI.2017.7992701-
dc.contributor.localauthorKwon, Dong-Soo-
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ME-Conference Papers(학술회의논문)
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