通过数字孪生与边缘计算，实现工业元宇宙中人机交互的低延迟高精度响应。
Task-Oriented Edge-Assisted Cross-System Design for Real-Time Human-Robot Interaction in Industrial Metaverse
- 分拆数字孪生为视觉与控制双功能，提升系统灵活性与性能。
- 在绘图任务中将误差降低至0.0101米，3D场景重建达PSNR 22.11、SSIM 0.8729。
- 适合高风险工业场景下对实时性与精度要求高的智能交互系统研发者。
工业元宇宙中的实时人机交互面临计算负载高、带宽有限和延迟严格等挑战。本文提出一种面向任务的边缘辅助跨系统框架，利用数字孪生（DTs）实现响应式交互。通过预测操作员动作，系统支持：1）主动渲染元宇宙以提供视觉反馈；2）预判远程设备控制。数字孪生被解耦为视觉显示与机器人控制两个虚拟功能，优化了性能与适应性。为增强泛化能力，引入人机协同模型无关元学习（HITL-MAML）算法，动态调整预测时长。在两项任务中验证了框架有效性：在基于轨迹的绘图控制任务中，加权均方根误差从0.0712米降至0.0101米；在核电站退役的实时3D场景重建任务中，取得PSNR 22.11、SSIM 0.8729、LPIPS 0.1298。结果表明该框架可在高风险工业环境中保障空间精度与视觉保真度。
原文摘要 · Abstract (English)
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.
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