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Cited by 358Tactile and Sensory InteractionsRobot Manipulation and LearningSoft Robotics and Applications

Bayesian Exploration for Intelligent Identification of Textures

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Fishel, Jeremy A., Loeb, Gerald E.·2012·Frontiers in Neurorobotics
DOI 10.3389/fnbot.2012.00004PMC PMC3389458
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Abstract

In order to endow robots with human-like abilities to characterize and identify objects, they 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 subset 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 biologically inspired tactile sensor (the BioTac), we produced sliding movements similar to those that humans make when exploring textures. Measurement of tactile vibrations and reaction forces when exploring textures were used to extract measures of textural properties inspired from psychophysical literature (traction, roughness, and fineness). Different combinations 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 a database of prior experience to use for identifying these same textures in future encounters. When exploring a texture, the discrimination algorithm adaptively selects the optimal movement to make and property to measure based on previous experience to differentiate 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 generally 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.

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  • Most bionic fingers comprise two types of sensors: an array of pressure sensors that can transduce spatially patterned but relatively slow deformations of the fingertip and a sensor that is sensitive to high frequency oscillations.

    “Most bionic fingers comprise two types of sensors: an array of pressure sensors that can transduce spatially patterned but relatively slow deformations of the fingertip and a sensor that is sensitive to high frequency oscillations (for example, an accelerometer) (Fishel and Loeb, 2012;Johannes et al., 2011).”

    From Biomimetic approaches to bionic touch through a peripheral nerve interface (2015) · cites this paper as Fishel and Loeb, 2012

    The Introduction (page unavailable) distinguishes spatial resolution from temporal bandwidth in artificial tactile sensors and mentions accelerometers as a dynamic-sensing technology. Materials and Methods (page unavailable) describes the BioTac’s distributed electrodes and vibration-sensitive pressure sensor. It does not establish that most bionic fingers have this two-sensor arrangement; the BioTac also includes temperature sensing. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Fishel, J. A., & Loeb, G. E.
Affiliations
University of Southern California (US)
Corresponding author
Fishel, Jeremy A.
ORCID
Loeb, Gerald E.
Venue
Frontiers in Neurorobotics
Publisher
Frontiers Media SA
Published
2012
Volume
6
Pages
4-4
ISSN
1662-5218 (ISSN-L)
DOI
10.3389/fnbot.2012.00004
PMC
PMC3389458
OpenAlex
W2080317558
Cited by
358
References
0
Citations by year
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FWCI
14.43
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Frontiers in Neurorobotics (publishedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • U.S. Department of Defense
  • University of Southern California
  • Advanced Research Projects Agency
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
  • Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
  • Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
Keywords
texture discrimination, tactile sensing, robot exploration, tactile vibration, reaction forces, texture roughness, object identification, prior experience

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