Continuous Orthogonal Mode Decomposition: Haptic Signal Prediction in Tactile Internet

2026-04-10

AI Summary

This work addresses the control instability in tactile internet systems caused by high latency and packet loss by proposing a bilateral predictive neural network architecture based on Continuous Orthogonal Mode Decomposition (MDA). By incorporating orthogonality constraints, the method explicitly extracts structured and interpretable tactile features, effectively mitigating mode mixing commonly observed in conventional decomposition approaches. Experimental results demonstrate that the proposed framework achieves tactile signal reconstruction accuracies of 98.6% and 97.3% at the human and robotic ends, respectively, with an inference latency as low as 0.065 milliseconds. These performance metrics significantly outperform existing methods and meet the stringent requirements of tactile teleoperation for both high precision and ultra-low latency.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Tactile Internet
• haptic signal prediction
• ultra-low latency
• mode overlapping
• real-time haptic teleoperation

Innovation

• Continuous-Orthogonal Mode Decomposition
• Mode-Domain Architecture
• haptic signal prediction
• orthogonality constraint
• Tactile Internet