# Task-Oriented Cross-System Design for Timely and Accurate Modeling in the Metaverse

arXiv: https://arxiv.org/abs/2309.05622 · HTML: https://mlsanity.com/paper/2309.05622/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2023.

## Built on by (most cited first, 7 of 7)

- [Toward Real-Time Edge AI: Model-Agnostic Task-Oriented Communication with Visual Feature Alignment](https://mlsanity.com/paper/2412.00862/) (2024)
- [Timeliness-Fidelity Tradeoff in 3D Scene Representations](https://mlsanity.com/paper/2407.16575/) (2024)
- [Task-Oriented Co-Design of Communication, Computing, and Control for Edge-Enabled Industrial Cyber-Physical Systems](https://mlsanity.com/paper/2503.08661/) (2025)
- [Task-Oriented Edge-Assisted Cross-System Design for Real-Time Human-Robot Interaction in Industrial Metaverse](https://mlsanity.com/paper/2508.20664/) (2025)
- [Task-Oriented Communications for 3D Scene Representation: Balancing Timeliness and Fidelity](https://mlsanity.com/paper/2509.17282/) (2025)
- [Spatial Computing Communications for Multi-User Virtual Reality in Distributed Mobile Edge Computing Network](https://mlsanity.com/paper/2510.14243/) (2025)
- [Domain-Adaptive Communication-Rate Optimization for Sim-to-Real Humanoid-Robot Wireless XR Teleoperation](https://mlsanity.com/paper/2605.19293/) (2026)

## Abstract (arXiv)

In this paper, we establish a task-oriented cross-system design framework to minimize the required packet rate for timely and accurate modeling of a real-world robotic arm in the Metaverse, where sensing, communication, prediction, control, and rendering are considered. To optimize a scheduling policy and prediction horizons, we design a Constraint Proximal Policy Optimization(C-PPO) algorithm by integrating domain knowledge from relevant systems into the advanced reinforcement learning algorithm, Proximal Policy Optimization(PPO). Specifically, the Jacobian matrix for analyzing the motion of the robotic arm is included in the state of the C-PPO algorithm, and the Conditional Value-at-Risk(CVaR) of the state-value function characterizing the long-term modeling error is adopted in the constraint. Besides, the policy is represented by a two-branch neural network determining the scheduling policy and the prediction horizons, respectively. To evaluate our algorithm, we build a prototype including a real-world robotic arm and its digital model in the Metaverse. The experimental results indicate that domain knowledge helps to reduce the convergence time and the required packet rate by up to 50%, and the cross-system design framework outperforms a baseline framework in terms of the required packet rate and the tail distribution of the modeling error.