Non-Matrix Tactile Sensors: How Can Be Exploited Their Local Connectivity For Predicting Grasp Stability?

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/90591
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Title: Non-Matrix Tactile Sensors: How Can Be Exploited Their Local Connectivity For Predicting Grasp Stability?
Authors: Zapata-Impata, Brayan S. | Gil, Pablo | Torres, Fernando
Research Group/s: Automática, Robótica y Visión Artificial
Center, Department or Service: Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal | Universidad de Alicante. Instituto Universitario de Investigación Informática
Keywords: Tactile detection | Tactile sensing | Robotic grasping | Predicting grasp stability | Tactile image | Artificial Intelligence | CNN
Knowledge Area: Ingeniería de Sistemas y Automática
Issue Date: 1-Oct-2018
Abstract: Tactile sensors supply useful information during the interaction with an object that can be used for assessing the stability of a grasp. Most of the previous works on this topic processed tactile readings as signals by calculating hand-picked features. Some of them have processed these readings as images calculating characteristics on matrix-like sensors. In this work, we explore how non-matrix sensors (sensors with taxels not arranged exactly in a matrix) can be processed as tactile images as well. In addition, we prove that they can be used for predicting grasp stability by training a Convolutional Neural Network (CNN) with them. We captured over 2500 real three-fingered grasps on 41 everyday objects to train a CNN that exploited the local connectivity inherent on the non-matrix tactile sensors, achieving 94.2% F1-score on predicting stability.
URI: http://hdl.handle.net/10045/90591
Language: eng
Type: info:eu-repo/semantics/conferenceObject
Rights: © The authors
Peer Review: no
Publisher version: https://arxiv.org/abs/1809.05551
Appears in Collections:INV - AUROVA - Comunicaciones a Congresos Internacionales

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