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

Zhou Zhao, Wenhao He, Zhenyu Lu

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.