用正交分解提升触觉信号预测精度，满足毫秒级实时传输需求
Continuous Orthogonal Mode Decomposition: Haptic Signal Prediction in Tactile Internet
- 基于正交模式分解构建双向预测网络，解决信号模式重叠问题
- 人类与机器人侧预测准确率分别达98.6%和97.3%
- 推理延迟低至0.065毫秒，适合高实时性触觉远程操作
触觉互联网要求亚毫秒级延迟和超高可靠性，任何延迟或丢包都可能破坏触觉控制。为此，我们提出模式域架构（MDA），一种双向预测神经网络，用于在人端和机器人端恢复缺失信号。不同于传统模型隐式从原始数据中提取特征，MDA采用新颖的连续正交模式分解框架。通过引入正交性约束，克服了现有分解方法普遍存在的“模式重叠”问题。实验结果表明，该结构化特征提取在人类侧和机器人侧分别达到98.6%和97.3%的高预测精度。此外，模型实现0.065毫秒的超低推理延迟，显著优于现有基准，满足触觉遥操作的严苛实时要求。
原文摘要 · Abstract (English)
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.
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