标题： 面向任务的边缘辅助跨系统设计用于工业元宇宙中的实时人机交互 显示英文标题

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

摘要： 实时工业元宇宙中的人机交互面临高计算负载、带宽有限和严格延迟等挑战。 本文提出了一种面向任务的边缘辅助跨系统框架，利用数字孪生（DTs）以实现响应式交互。 通过预测操作员的动作，该系统支持：1）主动的元宇宙渲染以提供视觉反馈，以及2）远程设备的预控。 数字孪生被解耦为两个虚拟功能——视觉显示和机器人控制——优化了性能和适应性。 为了增强泛化能力，我们引入了人机协同模型无关元学习（HITL-MAML）算法，该算法动态调整预测时间范围。 在两个任务上的评估证明了该框架的有效性：在基于轨迹的绘图控制任务中，加权RMSE从0.0712米降低到0.0101米；在核退役的实时3D场景表示任务中，达到了22.11的PSNR，0.8729的SSIM和0.1298的LPIPS。 这些结果表明该框架能够在实时、高风险的工业环境中确保空间精度和视觉保真度。 显示英文摘要

摘要： 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. To 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.