A Neuromorphic Incipient Slip Detection System Using Papillae Morphology

Detecting incipient slip enables early intervention to prevent object slippage and enhance robotic manipulation safety. Achieving this requires tactile interfaces with compliant, high-sensitivity skins that amplify local deformation differences and tactile sensing pipelines that preserve and exploit high-resolution spatio-temporal signals. This work presents an event-based tactile sensing system for incipient slip detection, built on the NeuroTac sensor, featuring an extruding papillae-based skin and a spiking convolutional neural network (SCNN). The SCNN achieves 94.33% accuracy in three-class classification (no slip, incipient slip, and gross slip) during slip conditions induced by sensor motion, delivering slightly higher accuracy than its ANN counterparts while maintaining a low theoretical computational cost. Under dynamic gravity-induced slip validation settings, after temporal smoothing of the SCNN's final-layer spike counts, the system detects incipient slip 360–1840 ms before actual gross slip across all trials. These results demonstrate the effectiveness of the proposed system and highlight its potential for future neuromorphic hardware deployment.