A general classifier of whisker data using stationary naive bayes: application to BIOTACT robots

Authors:
Nathan F. Lepora;Charles W. Fox;Mat Evans;Ben Mitchinson;Asma Motiwala;J. Charlie Sullivan;Martin J. Pearson;Jason Welsby;Tony Pipe;Kevin Gurney;Tony J. Prescott
Affiliations:
Department of Psychology, University of Sheffield, Sheffield, UK;Department of Psychology, University of Sheffield, Sheffield, UK;Department of Psychology, University of Sheffield, Sheffield, UK;Department of Psychology, University of Sheffield, Sheffield, UK;Department of Psychology, University of Sheffield, Sheffield, UK;Bristol Robotics Laboratory, Bristol, UK;Bristol Robotics Laboratory, Bristol, UK;Bristol Robotics Laboratory, Bristol, UK;Bristol Robotics Laboratory, Bristol, UK;Department of Psychology, University of Sheffield, Sheffield, UK;Department of Psychology, University of Sheffield, Sheffield, UK
Venue:
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
Year:
2011

Citing 5
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Abstract

A general problem in robotics is how to best utilize sensors to classify the robot's environment. The BIOTACT project (BIOmimetic Technology for vibrissal Active Touch) is a collaboration between biologists and engineers that has led to many distinctive robots with artificial whisker sensing capabilities. One problem is to construct classifiers that can recognize a wide range of whisker sensations rather than constructing different classifiers for specific features. In this article, we demonstrate that a stationary naive Bayes classifier can perform such a general classification by applying it to various robot experiments. This classifier could be a key component of a robot able to learn autonomously about novel environments, where classifier properties are not known in advance.