Using Gaussian Processes in Bayesian Robot Programming

Authors:
Fidel Aznar;Francisco A. Pujol;Mar Pujol;Ramón Rizo
Affiliations:
Depto. Ciencia de la Computación e Inteligencia Artificial,;Depto. Tecnología Informática y Computación, Universidad de Alicante, Alicante, España 03080;Depto. Ciencia de la Computación e Inteligencia Artificial,;Depto. Ciencia de la Computación e Inteligencia Artificial,
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Year:
2009

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Abstract

In this paper, we present an adaptation of Gaussian Processes for learning a joint probabilistic distribution using Bayesian Programming. More specifically, a robot navigation problem will be showed as a case of study. In addition, Gaussian Processes will be compared with one of the most popular techniques for machine learning: Neural Networks. Finally, we will discuss about the accuracy of these methods and will conclude proposing some future lines for this research.