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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Online Access: | https://www.mdpi.com/2218-6581/8/4/85 |
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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 |
_version_ |
1725294132785577984 |