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

arXiv: https://arxiv.org/abs/2508.20664 · HTML: https://mlsanity.com/paper/2508.20664/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 55 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Builds on (most cited first, 20 of 20)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment](https://mlsanity.com/paper/2401.07764/) (2024)
- [A Full Dive into Realizing the Edge-enabled Metaverse: Visions, Enabling Technologies,and Challenges](https://mlsanity.com/paper/2203.05471/) (2022)
- [Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission](https://mlsanity.com/paper/2404.09134/) (2024)
- [Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms](https://mlsanity.com/paper/2103.03697/) (2021)
- [Towards a Decentralized Metaverse: Synchronized Orchestration of Digital Twins and Sub-Metaverses](https://mlsanity.com/paper/2211.14686/) (2022)
- [On The Age Of Information In Status Update Systems With Packet Management](https://mlsanity.com/paper/1506.08637/) (2015)
- [Cybersecurity of Industrial Cyber-Physical Systems: A Review](https://mlsanity.com/paper/2101.03564/) (2021)
- [A Comprehensive Survey of the Tactile Internet: State of the art and Research Directions](https://mlsanity.com/paper/2009.12164/) (2020)
- [Task-Oriented Cross-System Design for Timely and Accurate Modeling in the Metaverse](https://mlsanity.com/paper/2309.05622/) (2023)
- [Prediction and Communication Co-design for Ultra-Reliable and Low-Latency Communications](https://mlsanity.com/paper/1909.05787/) (2019)
- [Resource Allocation for Augmented Reality Empowered Vehicular Edge Metaverse](https://mlsanity.com/paper/2212.01325/) (2022)
- [Communication Efficient Robotic Mixed Reality with Gaussian Splatting Cross-Layer Optimization](https://mlsanity.com/paper/2508.08624/) (2025)
- [Task-Oriented Metaverse Design in the 6G Era](https://mlsanity.com/paper/2306.03158/) (2023)
- [Prediction, Communication, and Computing Duration Optimization for VR Video Streaming](https://mlsanity.com/paper/1910.13884/) (2019)
- [Task Offloading with Multi-Tier Computing Resources in Next Generation Wireless Networks](https://mlsanity.com/paper/2205.13866/) (2022)
- [Real-Time Interactions Between Human Controllers and Remote Devices in Metaverse](https://mlsanity.com/paper/2407.16591/) (2024)
- [Goal-Oriented UAV Communication Design and Optimization for Target Tracking: A MachineLearning Approach](https://mlsanity.com/paper/2408.04358/) (2024)

## Abstract (arXiv)

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.