Human movement learning with dynamic movement primitives combined with mixture models

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dc.contributor.authorAlizadeh, T.
dc.contributor.authorCalinon, S.
dc.date.accessioned2015-10-22T11:30:36Z
dc.date.available2015-10-22T11:30:36Z
dc.date.issued2014
dc.description.abstractThe proposed research is to provide a probabilistic approach to learn human movements. Dynamical Movement Primitives (DMP) have been extensively used in robotics in order to learn human motions [1]. The DMP modulates a virtual spring with a learned non-linear force profile /(x), perturbing the system to make it follow a desired trajectory.ru_RU
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/422
dc.language.isoenru_RU
dc.publisherNazarbayev Universityru_RU
dc.subjectdynamical movement primitivesru_RU
dc.subjecthuman motionsru_RU
dc.subjectmixture modelsru_RU
dc.titleHuman movement learning with dynamic movement primitives combined with mixture modelsru_RU
dc.typeAbstractru_RU

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