Tactile-Driven Grasp Stability and Slip Prediction

One of the challenges in robotic grasping tasks is the problem of detecting whether a grip is stable or not. The lack of stability during a manipulation operation usually causes the slippage of the grasped object due to poor contact forces. Frequently, an unstable grip can be caused by an inadequate...

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Main Authors: Brayan S. Zapata-Impata, Pablo Gil, Fernando Torres
Format: Article
Language:English
Published: MDPI AG 2019-09-01
Series:Robotics
Subjects:
Online Access:https://www.mdpi.com/2218-6581/8/4/85
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spelling doaj-7a68bf9d124c4edca59cc87f3dd9d3752020-11-25T00:39:17ZengMDPI AGRobotics2218-65812019-09-01848510.3390/robotics8040085robotics8040085Tactile-Driven Grasp Stability and Slip PredictionBrayan S. Zapata-Impata0Pablo Gil1Fernando Torres2Automatics, Robotics, and Artificial Vision Lab (AUROVA), Computer Science Research Institute, University of Alicante, 03690 San Vicente del Raspeig, SpainAutomatics, Robotics, and Artificial Vision Lab (AUROVA), Computer Science Research Institute, University of Alicante, 03690 San Vicente del Raspeig, SpainAutomatics, Robotics, and Artificial Vision Lab (AUROVA), Computer Science Research Institute, University of Alicante, 03690 San Vicente del Raspeig, SpainOne of the challenges in robotic grasping tasks is the problem of detecting whether a grip is stable or not. The lack of stability during a manipulation operation usually causes the slippage of the grasped object due to poor contact forces. Frequently, an unstable grip can be caused by an inadequate pose of the robotic hand or by insufficient contact pressure, or both. The use of tactile data is essential to check such conditions and, therefore, predict the stability of a grasp. In this work, we present and compare different methodologies based on deep learning in order to represent and process tactile data for both stability and slip prediction.https://www.mdpi.com/2218-6581/8/4/85robotic graspingtactile perceptionintelligent manipulationstability detectionslip detection
collection DOAJ
language English
format Article
sources DOAJ
author Brayan S. Zapata-Impata
Pablo Gil
Fernando Torres
spellingShingle Brayan S. Zapata-Impata
Pablo Gil
Fernando Torres
Tactile-Driven Grasp Stability and Slip Prediction
Robotics
robotic grasping
tactile perception
intelligent manipulation
stability detection
slip detection
author_facet Brayan S. Zapata-Impata
Pablo Gil
Fernando Torres
author_sort Brayan S. Zapata-Impata
title Tactile-Driven Grasp Stability and Slip Prediction
title_short Tactile-Driven Grasp Stability and Slip Prediction
title_full Tactile-Driven Grasp Stability and Slip Prediction
title_fullStr Tactile-Driven Grasp Stability and Slip Prediction
title_full_unstemmed Tactile-Driven Grasp Stability and Slip Prediction
title_sort tactile-driven grasp stability and slip prediction
publisher MDPI AG
series Robotics
issn 2218-6581
publishDate 2019-09-01
description One of the challenges in robotic grasping tasks is the problem of detecting whether a grip is stable or not. The lack of stability during a manipulation operation usually causes the slippage of the grasped object due to poor contact forces. Frequently, an unstable grip can be caused by an inadequate pose of the robotic hand or by insufficient contact pressure, or both. The use of tactile data is essential to check such conditions and, therefore, predict the stability of a grasp. In this work, we present and compare different methodologies based on deep learning in order to represent and process tactile data for both stability and slip prediction.
topic robotic grasping
tactile perception
intelligent manipulation
stability detection
slip detection
url https://www.mdpi.com/2218-6581/8/4/85
work_keys_str_mv AT brayanszapataimpata tactiledrivengraspstabilityandslipprediction
AT pablogil tactiledrivengraspstabilityandslipprediction
AT fernandotorres tactiledrivengraspstabilityandslipprediction
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