PANDAS: Prediction and Detection of Accurate Slippage

Abstract

High-resolution tactile sensing and advanced computational models have accelerated progress in robotic grasping; however, real-time, stable manipulation of smooth and fragile objects still lags behind. The challenges are twofold: first, the robot must detect incipient slip at sub-millimeter scales in real time; second, the system must issue millisecond-level early warnings before true instability occurs so that the controller has sufficient time to react. To address these challenges, we propose PANDAS (Prediction AND Detection of Accurate Slippage), a framework that integrates a physics-informed, multimodal spatiotemporal network for slip detection with a probabilistic temporal reasoning module for forecasting near-future risk. Experimental results demonstrate that the proposed method achieves a slip sensitivity of 94.6%, a response latency of 28ms, and an early-warning lead time of 32ms. Moreover, under 5dB Gaussian noise, it maintains a high F1-score of 92.3%, validating its robustness, predictive capability, and suitability for edge deployment in dynamic, high-noise environments.