Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/93189
DC FieldValueLanguage
dc.contributor.authorFaria, Diego-
dc.contributor.authorMartins, Ricardo Filipe Alves-
dc.contributor.authorDias, Jorge-
dc.date.accessioned2021-02-23T22:52:55Z-
dc.date.available2021-02-23T22:52:55Z-
dc.date.issued2010-10-18-
dc.identifier.urihttp://hdl.handle.net/10316/93189-
dc.description.abstractIn this work we present a probabilistic approach to find motion patterns in manipulative tasks by looking for similarities among the relevant features along of the actions phases of a trajectories dataset. From multiples observations of human movements we can align all signals temporally to perform a learning process based on selection of relevant features by analyzing their probability distribution and finding correspondent features with high probability in each phase of the trajectories of a dataset. Using the spatio-temporal information of the learned features we can generate a generalized trajectory of the dataset using a polynomial regression to fit the features data by successive approximations. The smoothed trajectory can be used as a prototype/template for matching (1:1) or for classification (1:N) using Bayesian techniques to know if a new observation is similar to a specific task or to recognize a task. The intention here is to have an approach that is able to learn and generalize a specific movement by their patterns to be applied in the future for different contexts. We are not going through the imitation learning part, but we are focusing on the ability of learning to reach some intelligence to approximate a movement generalization, tasks that humans do in a natural and easy way.pt
dc.language.isoengpt
dc.rightsopenAccesspt
dc.titleLearning Motion Patterns from Multiple Observations along the Actions Phases of Manipulative Taskspt
dc.typeconferenceObjectpt
degois.publication.titleIROS 2010 - 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems- Session Workshop on Grasp Planning and Task Learning by Imitationpt
dc.peerreviewedyespt
dc.identifier.doi10.5281/zenodo.4553421-
dc.date.embargo2010-10-18*
uc.date.periodoEmbargo0pt
item.grantfulltextopen-
item.languageiso639-1en-
item.fulltextCom Texto completo-
crisitem.author.orcid0000-0001-7184-185X-
Appears in Collections:FCTUC Eng.Electrotécnica - Artigos em Livros de Actas
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