Non-Matrix Tactile Sensors: How Can Be Exploited Their Local Connectivity For Predicting Grasp Stability?
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Título: | Non-Matrix Tactile Sensors: How Can Be Exploited Their Local Connectivity For Predicting Grasp Stability? |
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Autor/es: | Zapata-Impata, Brayan S. | Gil, Pablo | Torres, Fernando |
Grupo/s de investigación o GITE: | Automática, Robótica y Visión Artificial |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal | Universidad de Alicante. Instituto Universitario de Investigación Informática |
Palabras clave: | Tactile detection | Tactile sensing | Robotic grasping | Predicting grasp stability | Tactile image | Artificial Intelligence | CNN |
Área/s de conocimiento: | Ingeniería de Sistemas y Automática |
Fecha de publicación: | 1-oct-2018 |
Resumen: | 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 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/conferenceObject |
Derechos: | © The authors |
Revisión científica: | no |
Versión del editor: | https://arxiv.org/abs/1809.05551 |
Aparece en las colecciones: | INV - AUROVA - Comunicaciones a Congresos Internacionales |
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1809.05551.pdf | Articulo principal | 5,08 MB | Adobe PDF | Abrir Vista previa |
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