Tactile Regrasp: Grasp Adjustments via Simulated Tactile Transformations
Francois R. Hogan, Maria Bauza, Oleguer Canal, Elliott Donlon, and, Alberto Rodriguez

TL;DR
This paper introduces a tactile-based regrasp control policy that uses simulated tactile transformations and a neural network to improve grasp success rates in robotic manipulation.
Contribution
It presents a novel method combining tactile sensing, simulated transformations, and deep learning for local grasp adjustments in robotics.
Findings
Grasp quality network predicts grasp outcomes with 85% accuracy on known objects.
Regrasp policy increases grasp success rate by 70% on test objects.
Method effectively improves grasp stability through tactile simulation and neural evaluation.
Abstract
This paper presents a novel regrasp control policy that makes use of tactile sensing to plan local grasp adjustments. Our approach determines regrasp actions by virtually searching for local transformations of tactile measurements that improve the quality of the grasp. First, we construct a tactile-based grasp quality metric using a deep convolutional neural network trained on over 2800 grasps. The quality of each grasp, a continuous value between 0 and 1, is determined experimentally by measuring its resistance to external perturbations. Second, we simulate the tactile imprints associated with robot motions relative to the initial grasp by performing rigid-body transformations of the given tactile measurements. The newly generated tactile imprints are evaluated with the learned grasp quality network and the regrasp action is chosen to maximize the grasp quality. Results show that the…
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