--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].

Industrial Metaverse leverages the Industrial Internet of Things (IIoT) to integrate data from diverse devices, employing federated learning and meta-computing to train models in a distributed manner while ensuring data privacy. Achieving an immersive experience for industrial Metaverse necessitates maintaining a balance between model quality and training latency. Consequently, a primary challenge in federated learning tasks is optimizing overall system performance by balancing model quality and training latency. This paper designs a satisfaction function that accounts for data size, Age of Information (AoI), and training latency. Additionally, the satisfaction function is incorporated into the utility functions to incentivize node participation in model training. We model the utility functions of servers and nodes as a two-stage Stackelberg game and employ a deep reinforcement learning approach to learn the Stackelberg equilibrium. This approach ensures balanced rewards and enhances the applicability of the incentive scheme for industrial Metaverse. Simulation results demonstrate that, under the same budget constraints, the proposed incentive scheme improves at least 23.7% utility compared to existing schemes without compromising model accuracy.

Training for industrial robots was once like a traditional school: rigid, predictable, and limited to practicing the same tasks over and over. Robots can learn in "virtual classrooms"--immersive environments in the industrial metaverse that use simulation, digital twins, and AI to mimic real-world conditions in detail. This digital world can provide an almost limitless training ground that mirrors real factories, warehouses, and production lines, allowing robots to practice tasks, encounter challenges, and develop problem-solving skills. What once took days or even weeks of real-world programming, with engineers painstakingly adjusting commands to get the robot to perform one simple task, can now be learned in hours in virtual spaces. This approach, known as simulation to reality (Sim2Real), blends virtual training with real-world application, bridging the gap between simulated learning and actual performance.

At CES last year, Sony teased an AR/VR headset prototype focused on "spatial content creation." And at the same time, Siemens announced it was working with Sony to use that same hardware, including the two new controllers it developed, for something it was calling the "industrial metaverse." That's a lot of buzzwords, but at CES 2025 both Siemens and Sony showed the headsets and associated software in action which helped clear up a lot of what the companies are trying to do here. During Sony's CES press conference, it announced its XYN brand of software and hardware solutions, with the headset being a key part of the equation. The XYN "spatial capture solution" uses mirrorless cameras to scan and make photorealistic 3D objects. Using the XYN headset, you can see those objects in 3D production software for animation, video games and other potential uses.

The rise of the industrial metaverse has brought digital twins (DTs) to the forefront. Blockchain-powered non-fungible tokens (NFTs) offer a decentralized approach to creating and owning these cloneable DTs. However, the potential for unauthorized duplication, or counterfeiting, poses a significant threat to the security of NFT-DTs. Existing NFT clone detection methods often rely on static information like metadata and images, which can be easily manipulated. To address these limitations, we propose a novel deep-learning-based solution as a combination of an autoencoder and RNN-based classifier. This solution enables real-time pattern recognition to detect fake NFT-DTs. Additionally, we introduce the concept of dynamic metadata, providing a more reliable way to verify authenticity through AI-integrated smart contracts. By effectively identifying counterfeit DTs, our system contributes to strengthening the security of NFT-based assets in the metaverse.

Annika Hauptvogel, head of technology and innovation management at Siemens, describes the industrial metaverse as "immersive, making users feel as if they're in a real environment; collaborative in real time; open enough for different applications to seamlessly interact; and trusted by the individuals and businesses that participate"--far more than simply a digital world. The industrial metaverse will revolutionize the way work is done, but it will also unlock significant new value for business and societies. By allowing businesses to model, prototype, and test dozens, hundreds, or millions of design iterations in real time and in an immersive, physics-based environment before committing physical and human resources to a project, industrial metaverse tools will usher in a new era of solving real-world problems digitally. "The real world is very messy, noisy, and sometimes hard to really understand," says Danny Lange, senior vice president of artificial intelligence at Unity Technologies, a leading platform for creating and growing real-time 3-D content. "The idea of the industrial metaverse is to create a cleaner connection between the real world and the virtual world, because the virtual world is so much easier and cheaper to work with."

In the ever-evolving digital world, the metaverse is no longer science fiction but a tangible reality with numerous potentials for organizations. For business it is crucial to understand the significance of this technology and its impact on the future of your organization and work. In this article we are exploring possibilities, opportunities and challenges of the Microsoft metaverse for business. Let's begin with a short description what the metaverse is, in our context. The metaverse connects our physical world to the digital one and vice versa.

The Metaverse is being touted as the next iteration of the internet, supporting ongoing online 3D virtual environments where virtual experiences, real-time 3D content and other related media are connected and accessible through VR/AR, as well as through classic devices such as PC or mobile. It's essentially an immersive Web3 internet – Web3 being the idea of a new kind of internet services that is built using decentralised blockchain. With this new technology, users can meet in virtual spaces, represent themselves as avatars and share virtual objects.. Gartner predicts that by 2026 a quarter of people will spend at least an hour a day in the metaverse for work, shopping, education, social or entertainment. And 30% of organisations will have products and services delivered via the metaverse. However, despite the predictions, a recent YouGov survey revealed that just 37% of UK adults claim to be confident about describing the metaverse to others.

Renault Group is the latest carmaker to announce significant digitalisation plans, confirming a move into the industrial metaverse. The technology will combine augmented and virtual realities across many platforms, enabling different digital interactions. The industrial metaverse could offer several benefits, including a fresh marketing opportunity in front of a new audience. According to Renault, the system will be based on four dimensions spanning mass data collection, digital twins, supply chains, and advanced technologies. Renault states the integration of the metaverse can offer a'better visibility of the work environment allowing actors to gain agility and autonomy in decision making.'

Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine learning paradigm, is a promising approach to enable edge intelligence in the emerging Industrial Metaverse. Even though many successful use cases have proved the feasibility of FL in theory, in the industrial practice of Metaverse, the problems of non-independent and identically distributed (non-i.i.d.) data, learning forgetting caused by streaming industrial data, and scarce communication bandwidth remain key barriers to realize practical FL. Facing the above three challenges simultaneously, this paper presents a high-performance and efficient system named HFEDMS for incorporating practical FL into Industrial Metaverse. HFEDMS reduces data heterogeneity through dynamic grouping and training mode conversion (Dynamic Sequential-to-Parallel Training, STP). Then, it compensates for the forgotten knowledge by fusing compressed historical data semantics and calibrates classifier parameters (Semantic Compression and Compensation, SCC). Finally, the network parameters of the feature extractor and classifier are synchronized in different frequencies (Layer-wiseAlternative Synchronization Protocol, LASP) to reduce communication costs. These techniques make FL more adaptable to the heterogeneous streaming data continuously generated by industrial equipment, and are also more efficient in communication than traditional methods (e.g., Federated Averaging). Extensive experiments have been conducted on the streamed non-i.i.d. FEMNIST dataset using 368 simulated devices. Numerical results show that HFEDMS improves the classification accuracy by at least 6.4% compared with 8 benchmarks and saves both the overall runtime and transfer bytes by up to 98%, proving its superiority in precision and efficiency.