aiqbench analysis
This research introduces a bilateral predictive neural network architecture utilizing Continuous-Orthogonal Mode Decomposition to restore haptic signals in the Tactile Internet. By enforcing orthogonality constraints, the model effectively eliminates mode overlapping issues common in state-of-the-art decomposition methods while achieving sub-millisecond inference latency. These advancements directly address the critical reliability and speed requirements necessary for stable remote haptic control systems.
Continuous Orthogonal Mode Decomposition: Haptic Signal Prediction in Tactile Internet
arXiv:2604.09446v2 Announce Type: replace-cross Abstract: 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.
Abstract: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.
Signal Processing (eess.SP); Machine Learning (cs.LG)
arXiv:2604.09446 [eess.SP]
(or arXiv:2604.09446v2 [eess.SP] for this version)
https://doi.org/10.48550/arXiv.2604.09446 Focus to learn more arXiv-issued DOI via DataCite
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