Conference Proceeding

A General Classifier of Whisker Data Using Stationary Naive Bayes: Application to BIOTACT Robots.

01/2011; DOI:10.1007/978-3-642-23232-9_2 In proceeding of: Towards Autonomous Robotic Systems - 12th Annual Conference, TAROS 2011, Sheffield, UK, August 31 - September 2, 2011. Proceedings
Source: DBLP

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.

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    ABSTRACT: Studies of decision making in animals suggest a neural mechanism of evidence accumulation for competing percepts according to Bayesian sequential analysis. This model of perception is embodied here in a biomimetic tactile sensing robot based on the rodent whisker system. We implement simultaneous perception of object shape and location using two psychological test paradigms: first, a free-response paradigm in which the agent decides when to respond, implemented with Bayesian sequential analysis; and second an interrogative paradigm in which the agent responds after a fixed interval, implemented with maximum likelihood estimation. A benefit of free-response Bayesian perception is that it allows tuning of reaction speed against accuracy. In addition, we find that large gains in decision performance are achieved with unforced re-sponses that allow null decisions on ambiguous data. Therefore free-response Bayesian perception offers benefits for artificial systems that make them more animal-like in behavior.
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