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The ability to detect slip, particularly incipient slip, enables roboticsystems to take corrective measures to prevent a grasped object from beingdropped. Therefore, slip detection can enhance the overall security of roboticgripping. However, accurately detecting incipient slip remains a significantchallenge. In this paper, we propose a novel learning-based approach to detectincipient slip using the PapillArray (Contactile, Australia) tactile sensor.The resulting model is highly effective in identifying patterns associated withincipient slip, achieving a detection success rate of 95.6% when tested with anoffline dataset. Furthermore, we introduce several data augmentation methods toenhance the robustness of our model. When transferring the trained model to arobotic gripping environment distinct from where the training data wascollected, our model maintained robust performance, with a success rate of96.8%, providing timely feedback for stabilizing several practical grippingtasks. Our project website:https://sites.google.com/view/incipient-slip-detection.

Robust Learning-Based Incipient Slip Detection using the PapillArray Optical Tactile Sensor for Improved Robotic Gripping