Tactile Regrasp: Grasp Adjustments via Simulated Tactile Transformations

Understand

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.