Continuous Orthogonal Mode Decomposition: Haptic Signal Prediction in Tactile Internet

TL;DR

This paper introduces a novel neural network architecture using continuous orthogonal mode decomposition for accurate, low-latency haptic signal prediction in the Tactile Internet, enhancing real-time teleoperation reliability.

Contribution

It proposes the Mode-Domain Architecture with a continuous orthogonal mode decomposition framework, improving feature extraction and prediction accuracy for haptic signals.

Findings

Achieved 98.6% prediction accuracy for human signals.

Achieved 97.3% prediction accuracy for robot signals.

Model inference latency of 0.065 ms outperforms existing benchmarks.

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.