# Continuous Orthogonal Mode Decomposition: Haptic Signal Prediction in Tactile Internet

arXiv: https://arxiv.org/abs/2604.09446 · HTML: https://mlsanity.com/paper/2604.09446/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Professor Forcing: A New Algorithm for Training Recurrent Networks](https://mlsanity.com/paper/1610.09038/) (2016)
- [Delay Bound Relaxation with Deep Learning-based Haptic Estimation for Tactile Internet](https://mlsanity.com/paper/2507.00571/) (2025)
- [Signal Prediction for Loss Mitigation in Tactile Internet: A Leader-Follower Game-Theoretic Approach](https://mlsanity.com/paper/2507.07692/) (2025)
- [Shapley Features for Robust Signal Prediction in Tactile Internet](https://mlsanity.com/paper/2509.21032/) (2025)

## Abstract (arXiv)

The Tactile Internet demands sub-millisecond latency and ultra-high reliability, as even slight latency or packet loss can destabilize haptic control. 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 implicitly extract features from raw data, MDA employs 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.