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Dynamically consistent probabilistic model for robot motion learning

AuthorsPardo, Diego; Rozo, Leonel ; Alenyà, Guillem ; Torras, Carme
Issue Date2012
CitationIROS 2012
AbstractThis work presents a probabilistic model for learning robot tasks from human demonstrations using kinesthetic teaching. The difference with respect to previous works is that a complete state of the robot is used to obtain a consistent representation of the dynamics of the task. The learning framework is based on hidden Markov models and Gaussian mixture regression, used for coding and reproducing the skills. Benefits of the proposed approach are shown in the execution of a simple self-crossing trajectory by a 7-DoF manipulator.
DescriptionPresentado al International Conference on Intelligent Robots and Systems Workshop on Learning and Interaction in Haptic Robots (IROS LIHR) celebrado en Portugal del 7 al 12 de octubre de 2012.
Publisher version (URL)http://www.iros2012.org/site/
Appears in Collections:(IRII) Comunicaciones congresos
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