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Tactile-Driven Grasp Stability and Slip Prediction

Automatics, Robotics, and Artificial Vision Lab (AUROVA), Computer Science Research Institute, University of Alicante, 03690 San Vicente del Raspeig, Spain
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These authors contributed equally to this work.
Robotics 2019, 8(4), 85; https://doi.org/10.3390/robotics8040085
Received: 15 July 2019 / Revised: 23 September 2019 / Accepted: 25 September 2019 / Published: 26 September 2019
(This article belongs to the Special Issue Robotics in Spain 2019)
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. View Full-Text
Keywords: robotic grasping; tactile perception; intelligent manipulation; stability detection; slip detection robotic grasping; tactile perception; intelligent manipulation; stability detection; slip detection
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MDPI and ACS Style

Zapata-Impata, B.S.; Gil, P.; Torres, F. Tactile-Driven Grasp Stability and Slip Prediction. Robotics 2019, 8, 85.

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