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dc.contributorAngulo Bahón, Cecilio
dc.contributor.authorArbones Clua, Marc
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2017-10-25T18:11:43Z
dc.date.available2017-10-25T18:11:43Z
dc.date.issued2017-09-13
dc.identifier.urihttp://hdl.handle.net/2117/109222
dc.description.abstractThe complexity in modeling human movement increases as the dimensionality of these movement grows. Since searching more precision and flexibility involves more variables in the model. Dynamic Movement Primitives (DMP) have shown the ability to generate joint movements with high complexity. However, the problem remains in the interaction between several joints since DMP alone is not able to deal with it. To solve this problem a new model called autoencoded dynamic movement primitive (AE- DMP) is introduced in the work "Efficient movement representation by embedding DynamicMovement Primitives in Deep Autoencoders"[2]. The proposed approach uses autoencoder in order to find a representation of the movement in a latent space. Consequently, the DMPmodel is able to reconstruct the complete movement. In thisMaster Thesis we will study the implementation of this model and study its performance. All the features stated in the original paper are checked, as multiple movements, sparsity and reconstruction of missing or corrupted data.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshRobotics
dc.titleDeep learning: creating bridges between DMPs in autoencoders and recurrent neural networks
dc.typeMaster thesis
dc.subject.lemacXarxes neuronals (Informàtica)
dc.subject.lemacRobòtica
dc.identifier.slugETSEIB-240.126949
dc.rights.accessOpen Access
dc.date.updated2017-10-12T05:34:10Z
dc.audience.educationlevelMàster
dc.audience.mediatorEscola Tècnica Superior d'Enginyeria Industrial de Barcelona


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