Tactile-Based Grasping Stability Prediction Based on Human Grasp Demonstration for Robot Manipulation

Abstract

To minimize irrelevant and redundant information in tactile data and harness the dexterity of human hands. In this paper, we introduce a novel binary classification network with normalized differential convolution (NDConv) layers. Our method leverages the recent progress in visual-based tactile sensing to significantly improve the accuracy of grasp stability prediction. First, we collect a dataset from human demonstration by grasping 15 different daily objects. Then, we rethink pixel correlation and design a novel NDConv layer to fully utilize spatio-temporal information. Finally, the classification network not only achieves a real-time temporal sequence prediction but also obtains an average classification accuracy of 92.97%. The experimental results show that the network can hold a high classification accuracy even when facing unseen objects.