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Detection of slip during object grasping and manipulation plays a vital rolein object handling. Existing solutions largely depend on visual information todevise a strategy for grasping. Nonetheless, in order to achieve proficiencyakin to humans and achieve consistent grasping and manipulation of unfamiliarobjects, the incorporation of artificial tactile sensing has become a necessityin robotic systems. In this work, we propose a novel physics-informed,data-driven method to detect slip continuously in real time. The GelSight Mini,an optical tactile sensor, is mounted on custom grippers to acquire tactilereadings. Our work leverages the inhomogeneity of tactile sensor readingsduring slip events to develop distinctive features and formulates slipdetection as a classification problem. To evaluate our approach, we testmultiple data-driven models on 10 common objects under different loadingconditions, textures, and materials. Our results show that the bestclassification algorithm achieves an average accuracy of 99%. We demonstratethe application of this work in a dynamic robotic manipulation task in whichreal-time slip detection and prevention algorithm is implemented.

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