Explainable AI-assisted low-latency haptic feedback prediction for human-to-machine applications over passive optical networks

Abstract

Human-to-machine applications, such as robotic teleoperation, require ultra-low latency for real-time interactions. In passive optical networks (PONs), edge AI servers at the optical line terminal can predict haptic feedback in advance based on control signals, thereby enhancing the immersive experience. To further reduce latency while preserving predictive performance, this paper proposes an eXplainable AI-assisted low-latency haptic feedback prediction framework, using XAI for feature selection to reduce inference time. In a 50G-PON network, the framework achieves the lowest round-trip delay and packet delay variation among evaluated approaches. Extensive simulations show a 64.9% reduction in inference time, 15.5% in round-trip delay, and 15.1% in delay variation under a typical traffic load of 0.5, demonstrating its effectiveness for next-generation AI-assisted optical networks.