Reliable collaborative inference for embodied AI empowered by application-aware IPv6 networking

Constrained by edge computing resources and model capacity, embodied agents often struggle to maintain high inference accuracy in complex dynamic scenarios when performing real-time visual perception tasks. Although offloading difficult samples to the cloud for collaborative inference is an effective way to mitigate this problem, existing solutions generally overlook the challenges posed by network transmission uncertainty to inference timeliness. The traditional network layer lacks the ability to perceive the semantics of application-layer visual tasks and cannot distinguish the urgency of inference requests. Consequently, high-risk difficult samples compete for bandwidth with ordinary traffic in congested environments, undermining the reliability of embodied intelligence systems. To address these challenges, this paper proposes a Reliable Application-aware Collaborative Inference (RACI) framework, which establishes a cross-layer mechanism linking application semantics to network scheduling. The core idea of RACI is to bridge the gap between the application layer and the network layer: at the application layer, an adaptive decision mechanism based on Scene Ambiguity Score (SAS) is designed to identify difficult samples requiring offloading with low computational overhead by jointly measuring edge-side inference uncertainty and scene object density; at the network layer, the offloading decision semantics are encoded into the Flow Label field of IPv6 packets, enabling network devices to identify the urgency of inference traffic and perform differentiated flow scheduling. Experiments in object detection scenarios show that RACI achieves detection accuracy improvements in scenarios where edge model capabilities are insufficient, and effectively supports low-latency transmission of critical inference traffic under network congestion.