Non-Matrix Tactile Sensors: How Can Be Exploited Their Local Connectivity For Predicting Grasp Stability?
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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|
|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.|
|Rights:||© The authors|
|Appears in Collections:||INV - AUROVA - Comunicaciones a Congresos Internacionales|
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