Structural and input reduction in a ESN for robotic navigation tasks

Abstract

This manuscript aims at showing the effects of feature selection and manifold reduction methods in dealing with the wall-following problem in mobile robotics, a well-known nonlinearly separable classification problem in which sensor recordings are associated to controlled motor responses. The capabilities of state manifold reduction in Echo State Networks (ESNs) through Laplacian Eigenmaps (LEs) are described in terms of noise rejection over the trained weights. Furthermore, various machine learning-based and data mining-based methodologies are applied to show the advantages of using the most informative contents drawn from the original sensor readings.