Learning to Detect Slip through Tactile Estimation of the Contact Force Field and its Entropy

Abstract

Detection of slip during object grasping and manipulation plays a vital role in object handling. Existing solutions primarily rely on visual information to devise a strategy for grasping. However, for robotic systems to attain a level of proficiency comparable to humans, especially in consistently handling and manipulating unfamiliar objects, integrating artificial tactile sensing is increasingly essential. We introduce a novel physics-informed, data-driven approach to detect slip continuously in real time. We employ the GelSight Mini, an optical tactile sensor, attached to custom-designed grippers to gather tactile data. Our work leverages the inhomogeneity of tactile sensor readings during slip events to develop distinctive features and formulates slip detection as a classification problem. To evaluate our approach, we test multiple data-driven models on 10 common objects under different loading conditions, textures, and materials. Our results show that the best classification algorithm achieves a high average accuracy of 95.61%. We further illustrate the practical application of our research in dynamic robotic manipulation tasks, where our real-time slip detection and prevention algorithm is implemented.