  The ability to detect slip, particularly incipient slip, enables robotic
systems to take corrective measures to prevent a grasped object from being
dropped. Therefore, slip detection can enhance the overall security of robotic
gripping. However, accurately detecting incipient slip remains a significant
challenge. In this paper, we propose a novel learning-based approach to detect
incipient slip using the PapillArray (Contactile, Australia) tactile sensor.
The resulting model is highly effective in identifying patterns associated with
incipient slip, achieving a detection success rate of 95.6% when tested with an
offline dataset. Furthermore, we introduce several data augmentation methods to
enhance the robustness of our model. When transferring the trained model to a
robotic gripping environment distinct from where the training data was
collected, our model maintained robust performance, with a success rate of
96.8%, providing timely feedback for stabilizing several practical gripping
tasks. Our project website:
https://sites.google.com/view/incipient-slip-detection.
