131457 20240209155915.0 doi 10.1109/IROS.2010.5650297 sideral 132277 ART-2010-132277 eng Osório, Pedro Gaussian mixture models for affordance learning using Bayesian Networks 2010 Affordances 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. Access copy available to the general public Unrestricted info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Bernardino, A Martinez-Cantin, R (orcid)0000-0002-6741-844X Santos-Victor, José 2010 (2010), 4432-4437 Proc. IEEE/RSJ Int. Conf. Intell. Rob. Syst. Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems 2153-0858 540557 http://zaguan.unizar.es/record/131457/files/texto_completo.pdf Postprint 3205426 http://zaguan.unizar.es/record/131457/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:131457 articulos driver 2024-02-09-14:29:58 ARTICLE