Learning to detect slip through tactile estimation of the contact force field and its entropy properties

Slip detection during object grasping and manipulation plays a vital role in object handling. Visual feedback can help devise a strategy for grasping. However, for robotic systems to attain a proficiency comparable to humans, integrating artificial tactile sensing is increasingly essential, especially in consistently handling unfamiliar objects. We introduce a novel physics-informed, data-driven approach to detect slip continuously for control-oriented tasks. Our work leverages the inhomogeneity of tactile sensor readings during slip events to develop distinct features and formulates slip detection as a classification problem. We test multiple data-driven models on 10 common objects under different loading conditions, textures, and materials to evaluate our approach. The resulting best classification algorithm achieves a high average accuracy of 95.61%. Practical application in dynamic robotic manipulation demonstrates the effectiveness of the proposed real-time slip detection and prevention.