Continuous Orthogonal Mode Decomposition: Haptic Signal Prediction in Tactile Internet
2026-04-10 • Machine Learning
Machine Learning
AI summaryⓘ
The authors address the challenge of very fast and reliable communication needed for touch-based internet, where delays or lost data can cause problems in controlling devices remotely. They introduce a new neural network design called Mode-Domain Architecture (MDA) that predicts and fills in missing signals on both the human and robot sides. Instead of just processing raw data, their method breaks down signals into parts with less overlap, improving accuracy and speed. Their approach achieves very high prediction accuracy and runs much faster than existing methods, meeting the strict speed needs of remote touch control.
Tactile InternetLatencyHaptic TeleoperationPredictive Neural NetworksMode DecompositionOrthogonality ConstraintSignal RestorationInference LatencyBilateral ControlReal-time Systems
Authors
Mohammad Ali Vahedifar, Mojtaba Nazari, Qi Zhang
Abstract
The Tactile Internet demands sub-millisecond latency and ultra-high reliability, as high latency or packet loss could lead to haptic control instability. To address this, we propose the Mode-Domain Architecture (MDA), a bilateral predictive neural network architecture designed to restore missing signals on both the human and robot sides. Unlike conventional models that extract features implicitly from raw data, MDA utilizes a novel Continuous-Orthogonal Mode Decomposition framework. By integrating an orthogonality constraint, we overcome the pervasive issue of "mode overlapping" found in state-of-the-art decomposition methods. Experimental results demonstrate that this structured feature extraction achieves high prediction accuracies of 98.6% (human) and 97.3% (robot). Furthermore, the model achieves ultra-low inference latency of 0.065 ms, significantly outperforming existing benchmarks and meeting the stringent real-time requirements of haptic teleoperation.