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ON DIFFERENT LEARNING APPROACHES WITH ECHO STATE NETWORKS FOR LOCALIZATION OF SMALL MOBILE ROBOTS

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Title: ON DIFFERENT LEARNING APPROACHES WITH ECHO STATE NETWORKS FOR LOCALIZATION OF SMALL MOBILE ROBOTS

Authors: Antonelo, Eric Aislan; Schrauwen, Benjamin

Abstract: Animals such as rats have innate and robust localization capabilities which allow them to navigate to goals in a maze. The rodent’s hippocampus, with the so called place cells, is responsible for such spatial processing. This work seeks to model these place cells using either supervised or unsupervised learning techniques. More specifically, we use a randomly generated recurrent neural network (the reservoir) as a non-linear temporal kernel to expand the input to a rich dynamic space. The reservoir states are linearly combined (using linear regression) or, in the unsupervised case, are used for extracting slowly-varying features from the input to form place cells (the architectures are organized in hierarchical layers). Experiments show that a small mobile robot with cheap and low-range distance sensors can learn to self-localize in its environment with the proposed systems.

Keywords: Robot localization; place cells; hippocampus; mobile robots; reservoir computing

Pages: 5

DOI: 10.21528/CBRN2009-067

Paper as PDF: 067_CBRN2009.pdf

BibTex File: 067_CBRN2009.bib