  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.
