Uncertainty-Aware Haptic Signal Estimation for Reliable and Resource Efficient Tactile Internet

Abstract

The Tactile Internet aims to enable real-time remote haptic interaction; however, the high sampling rates required for transparency in haptic control often lead to severe congestion in multi-user wireless environments. This paper proposes the Agile AI-empowered Haptic (A2HAP) framework, which integrates VarxHAP, a novel probabilistic neural network for joint force and uncertainty estimation, with an error-resilient controller. By employing a hierarchical gating architecture, the system dynamically adapts transmission thresholds to balance model confidence against reliability targets. Simulation results demonstrate that A2HAP suppresses packet rates by up to 45% during peak traffic and reduces resource block consumption by 25% on average. Consequently, the framework supports a 20% increase in user capacity compared to state-of-the-art methods while maintaining the ultra-reliability required for stable teleoperation.