000131457 001__ 131457
000131457 005__ 20240209155915.0
000131457 0247_ $$2doi$$a10.1109/IROS.2010.5650297
000131457 0248_ $$2sideral$$a132277
000131457 037__ $$aART-2010-132277
000131457 041__ $$aeng
000131457 100__ $$aOsório, Pedro
000131457 245__ $$aGaussian mixture models for affordance learning using Bayesian Networks
000131457 260__ $$c2010
000131457 5060_ $$aAccess copy available to the general public$$fUnrestricted
000131457 5203_ $$aAffordances are fundamental descriptors of relationships between actions, objects and effects. They provide the means whereby a robot can predict effects, recognize actions, select objects and plan its behavior according to desired goals. This paper approaches the problem of an embodied agent exploring the world and learning these affordances autonomously from its sensory experiences. Models exist for learning the structure and the parameters of a Bayesian Network encoding this knowledge. Although Bayesian Networks are capable of dealing with uncertainty and redundancy, previous work considered complete observability of the discrete sensory data, which may lead to hard errors in the presence of noise. In this paper we consider a probabilistic representation of the sensors by Gaussian Mixture Models (GMMs) and explicitly taking into account the probability distribution contained in each discrete affordance concept, which can lead to a more correct learning.
000131457 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000131457 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000131457 700__ $$aBernardino, A
000131457 700__ $$0(orcid)0000-0002-6741-844X$$aMartinez-Cantin, R
000131457 700__ $$aSantos-Victor, José
000131457 773__ $$g2010 (2010), 4432-4437$$pProc. IEEE/RSJ Int. Conf. Intell. Rob. Syst.$$tProceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems$$x2153-0858
000131457 8564_ $$s540557$$uhttps://zaguan.unizar.es/record/131457/files/texto_completo.pdf$$yPostprint
000131457 8564_ $$s3205426$$uhttps://zaguan.unizar.es/record/131457/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000131457 909CO $$ooai:zaguan.unizar.es:131457$$particulos$$pdriver
000131457 951__ $$a2024-02-09-14:29:58
000131457 980__ $$aARTICLE