Gaussian mixture models for affordance learning using Bayesian Networks

Osório, Pedro ; Bernardino, A ; Martinez-Cantin, R ; Santos-Victor, José

Resumen: 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.

Idioma: Inglés

DOI: 10.1109/IROS.2010.5650297

Año: 2010

Publicado en: Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems 2010 (2010), 4432-4437
ISSN: 2153-0858

Tipo y forma: Artículo (PostPrint)