Task-Oriented Edge-Assisted Cross-System Design for Real-Time Human-Robot Interaction in Industrial Metaverse

--Real-time human-device interaction in industrial Metaverse faces challenges such as high computational load, limited bandwidth, and strict latency. This paper proposes a task-oriented edge-assisted cross-system framework using digital twins (DTs) to enable responsive interactions. By predicting operator motions, the system supports: 1) proactive Metaverse rendering for visual feedback, and 2) preemptive control of remote devices. The DTs are decoupled into two virtual functions--visual display and robotic control--optimizing both performance and adaptability. T o enhance generalizability, we introduce the Human-In-The-Loop Model-Agnostic Meta-Learning (HITL-MAML) algorithm, which dynamically adjusts prediction horizons. Evaluation on two tasks demonstrates the framework's effectiveness: in a Trajectory-Based Drawing Control task, it reduces weighted RMSE from 0.0712 m to 0.0101 m; in a real-time 3D scene representation task for nuclear decommissioning, it achieves a PSNR of 22.11, SSIM of 0.8729, and LPIPS of 0.1298. These results show the framework's capability to ensure spatial precision and visual fidelity in real-time, high-risk industrial environments. Industrial Metaverse represents an integrated virtual ecosystem that extends the concept of the Metaverse to specific industrial sectors, merging physical and digital realms. It explores the transformative potential of teleoperation, real-time collaboration, and synchronization within high-risk industries, driving substantial advancements in industrial operations [1]. Digital twins (DTs) are a key enabler within the larger framework of industrial Metaverse, facilitating real-time data interaction and providing highly accurate virtual models of physical assets [2].