Learning-Based Slip Detection for Dexterous Manipulation Using GelStereo Sensing

Abstract

Endowing the robot with tactile perception can effectively improve manipulation dexterity, along with various benefits of human-like touch. Using GelStereo (GS) tactile sensing, which gives high-resolution contact geometry information, including 2-D displacement field, and 3-D point cloud of the contact surface, we present a learning-based slip detection system in this study. The results reveal that the well-trained network achieves 95.79% accuracy on the never-seen testing dataset, which surpasses the current model-based and learning-based methods using visuotactile sensing. We also propose a general framework for slip feedback adaptive control for dexterous robot manipulation tasks. The experimental results show the effectiveness and efficiency of the proposed control framework using GS tactile feedback when deployed on real-world grasping and screwing manipulation tasks on various robot setups.