Tactile Execution Monitoring of Robotic Manipulation Via Time-Series Based Predictive Encoding

Abstract

Tactile manipulation is a prominent and growing field where most research focuses on developing generalized manipulation policies. However, tactile execution monitoring - the ability to reliably evaluate manipulation at the skill level - is often overlooked, despite being critical for unsupervised deployment in both human-centered environments and industry, where strict safety and quality requirements apply. We propose the Tactile Predictive Encoding Model (TPEM), a time-series tactile perception framework inspired by human predictive encoding that enables real-time anomaly detection from skilllevel sensory data. TPEM extends predictive coding concepts from global task modeling to precise monitoring of contact-rich manipulation beyond the capabilities of visual sensing. We evaluate TPEM on three representative tasks: key insertion and turning, peg-in-hole insertion, and screw insertion and tightening using an industrial assembly model. Experiments on a tactile-enabled Franka Emika robot under realistic noise conditions show robust anomaly detection with zero false positives. Comparison with baseline methods - including Support Vector Machines (SVM), Hidden Markov Models (HMM), and recurrent generative models such as LSTM-VAE — demonstrates that TPEM consistently outperforms state-of-the-art approaches in contact-rich skill-level execution monitoring.