Literature DB >> 27168603

Biomimetic Active Touch with Fingertips and Whiskers.

Nathan F Lepora.   

Abstract

This study provides a synthetic viewpoint that compares, contrasts, and draws commonalities for biomimetic perception over a range of tactile sensors and tactile stimuli. Biomimetic active perception is formulated from three principles: (i) evidence accumulation based on leading models of perceptual decision making; (ii) action selection with an evidence-based policy, here based on overt focal attention; and (iii) sensory encoding of evidence based on neural coding. Two experiments with each of three biomimetic tactile sensors are considered: the iCub (capacitive) fingertip, the TacTip (optical) tactile sensor, and BIOTACT whiskers. For each sensor, one experiment considers a similar task (perception of shape and location) and the other a different tactile perception task. In all experiments, active perception with a biomimetic action selection policy based on focal attention outperforms passive perception with static or random action selection. The active perception also consistently reaches superresolved accuracy (hyperacuity) finer than the spacing between tactile elements. Biomimetic active touch thus offers a common approach for biomimetic tactile sensors to accurately and robustly characterize and explore non-trivial, uncertain environments analogous to how animals perceive the natural world.

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Year:  2016        PMID: 27168603     DOI: 10.1109/TOH.2016.2558180

Source DB:  PubMed          Journal:  IEEE Trans Haptics        ISSN: 1939-1412            Impact factor:   2.487


  3 in total

1.  3D Contact Position Estimation of Image-Based Areal Soft Tactile Sensor with Printed Array Markers and Image Sensors.

Authors:  Jong-Il Lee; Suwoong Lee; Hyun-Min Oh; Bo Ram Cho; Kap-Ho Seo; Min Young Kim
Journal:  Sensors (Basel)       Date:  2020-07-07       Impact factor: 3.576

2.  Using 3D Convolutional Neural Networks for Tactile Object Recognition with Robotic Palpation.

Authors:  Francisco Pastor; Juan M Gandarias; Alfonso J García-Cerezo; Jesús M Gómez-de-Gabriel
Journal:  Sensors (Basel)       Date:  2019-12-05       Impact factor: 3.576

3.  Identifying the Strength Level of Objects' Tactile Attributes Using a Multi-Scale Convolutional Neural Network.

Authors:  Peng Zhang; Guoqi Yu; Dongri Shan; Zhenxue Chen; Xiaofang Wang
Journal:  Sensors (Basel)       Date:  2022-03-01       Impact factor: 3.576

  3 in total

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