Bayesian exploration for intelligent identification of textures

329Citations
Citations of this article
313Readers
Mendeley users who have this article in their library.

Abstract

In order to endow robots with human-like abilities to characterize and identify objects, the must be provided with tactile sensors and intelligent algorithms to select, control, and interpret data from useful exploratory movements. Humans make informed decisions on the sequence of exploratory movements that would yield the most information for the task, depending on what the object may be and prior knowledge of what to expect from possible exploratory movements. This study is focused on texture discrimination, a sub set of a much larger group of exploratory movements and percepts that humans use to discriminate, characterize, and identify objects. Using a testbed equipped with a biologi cally inspired tactile sensor (the BioTac), we produced sliding movements similar to those that humans make when exploring textures. Measurement of tactile vibrations and reac tion forces when exploring textures were used to extract measures of textural propertie inspired from psychophysical literature (traction, roughness, and fineness). Different com binations of normal force and velocity were identified to be useful for each of these three properties. A total of 117 textures were explored with these three movements to create database of prior experience to use for identifying these same textures in future encoun ters. When exploring a texture, the discrimination algorithm adaptively selects the optima movement to make and property to measure based on previous experience to differenti ate the texture from a set of plausible candidates, a process we call Bayesian exploration Performance of 99.6% in correctly discriminating pairs of similar textures was found to exceed human capabilities. Absolute classification from the entire set of 117 textures gen erally required a small number of well-chosen exploratory movements (median =5) and yielded a 95.4% success rate. The method of Bayesian exploration developed and tested in this paper may generalize well to other cognitive problems.

Cite

CITATION STYLE

APA

Fishel, J. A., & Loeb, G. E. (2012). Bayesian exploration for intelligent identification of textures. Frontiers in Neurorobotics, (JUNE), 1–20. https://doi.org/10.3389/fnbot.2012.00004

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free