How Extended Reality (XR) and Robotics Play Together

There was a plethora of announcements this year at CES. Some were expected, but others proved to be more intriguing than initially anticipated. For example, NVIDIA's keynote presented various robotics and Artificial Intelligence (AI) initiatives, underscoring its interest in self-driving cars and industrial robotics. Although NVIDIA did not explicitly use the term “XR,” the integration of Apple Vision Pro in its workflows indicates that XR capabilities are very much part of its plans. The Omniverse platform, which enables users to create and interact with Three-Dimensional (3D) virtual worlds in real time, will be central to the upcoming projects NVIDIA speaks of. Omniverse relies on spatial computing data to train AI models and simulate real-world scenarios for robotics. The Apple Vision Pro Mixed Reality (MR) headset appeared in NVIDIA's promotional images, indicating that Apple’s device has a role in NVIDIA’s workflows. According to NVIDIA, developers can pair Vision Pro with the Omniverse platform to capture human movements as 3D spatial data. This data can then be used to create digital twins of human actions for robots to emulate. Additionally, NVIDIA highlighted a new feature called “Omniverse Spatial Streaming to Apple Vision Pro,” designed to help developers build immersive industrial applications. This followed earlier announcements regarding Omniverse Cloud Application Programming Interfaces (APIs), which allow Apple Vision Pro users to stream high-fidelity, AI-enhanced 3D content through cloud-assisted computing.

It’s important to understand what this actually means. Let’s imagine a busy factory floor running or fine-tuning a new assembly line. Normally, you’d have to physically rearrange machines, test them, and hope everything goes smoothly—a process that can be both time-consuming and expensive. With XR headsets like the Apple Vision Pro and NVIDIA’s Omniverse platform, companies can simulate these settings virtually first. You can watch how robots interact with their environment in real time, spot potential issues, and refine tasks before committing to costly real-world changes. For segments like automated manufacturing or driverless cars, precision is everything. Using the Vision Pro’s spatial computing features to capture more accurate data means robots and AI systems will have a better and more reliable “view” of their surroundings. That can lead to safer autonomous driving, more efficient assembly lines, and fewer quality-control errors—all thanks to digital models built on higher-fidelity data backed up by AI-powered simulation.

Another notable development at CES was the growing collaboration among large tech companies. Many are realizing they can’t deliver their desired product alone and are joining forces to create cross-platform solutions through partnerships. A good example is the collaboration among Qualcomm, Google, Samsung, and others for Android XR. These collaborations should encourage standardization (think OpenUSD file formats) for more types and sources of data, and improve compatibility across devices—all of which can help boost innovation in both robotics and XR tech. Standardized data formats is incredibly important for data-complex and accuracy-sensitive environments—capturing data can be expensive and time consuming in the first place, and losing that data through unsupported formats or platforms is obviously a waste.

As these technologies mature, it’s likely we’ll see XR and digital twins in more and more industries, from healthcare training to logistics. Of course, there are hurdles to overcome. High-end headsets like the Vision Pro aren’t cheap, and many smaller businesses might lack the network infrastructure to handle advanced cloud-based simulations. Omniverse, specifically, does support more than just the Apple Vision Pro, including the Meta Quest lineup, so lower-cost headsets are available—though the level of data accuracy required by a use case like robotics simulation may demand more expensive and capable headsets. Beyond cost and infrastructure, adoption itself takes time because many XR devices still require a fair bit of setup and training. However, this is improving. As general knowledge and understanding of XR grows, device usability improves and hardware prices come down, more companies will be experimenting and adopting more frequently.

Some practical steps that companies and developers can implement to ensure effective deployment are as follows:

1. While XR headsets like the Vision Pro promise efficiency gains, the initial outlay can be significant. Conduct a thorough cost-benefit analysis, and factor in potential savings from reduced physical trials and improved accuracy to determine whether early adoption makes financial sense for your organization. Value from these devices comes in two ways: direct use value through applications like training and workflow guidance, and indirect value through captured data (like spatial data leveraged for robotics).
2. Rather than diving headfirst into a full-scale deployment, consider smaller pilot initiatives first. This allows teams to test real-time simulations, get comfortable with new workflows, and gather data on the impact of XR integration before committing to larger rollouts. Robotics and XR are traditionally separate markets, so there are not likely to be existing integrations outside of broad solutions like Omniverse.
3. Implementing XR headsets and simulation platforms is not just about hardware and software; it is also about management. Involve employees in the process from the beginning, offer hands-on demos, gather feedback, and identify training needs. Early engagement fosters a smoother transition and boosts user acceptance, especially as XR becomes increasingly integrated with robotics for training, remote operation, and process optimization.
4. High-quality simulations require robust network infrastructure, especially if they are cloud-based or demand low-latency connections. Evaluate your current hardware and connectivity to ensure it can handle high-bandwidth, real-time data. Where possible, anticipate future growth in XR usage and plan upgrades accordingly, particularly as XR-driven simulations play a crucial role in robotics for testing automation, remote control, and AI-driven decision-making.
5. Many XR devices still have user-interface quirks and can be complex to integrate into well-established workflows. Consider phased adoption to allow teams time to adapt. This also provides room to refine processes before scaling up, minimizing disruption to day-to-day operations, which is especially important in robotics, where XR can enhance human-robot collaboration, remote monitoring, and operational efficiency.

In summary, adopting XR technologies in tandem with other platforms can streamline development, refine training programs, and enable powerful digital twins. However, organizations should balance these benefits against factors such as cost, infrastructure needs, and employee readiness. By starting small and forging strategic alliances, companies stand to realize substantial long-term gains in productivity and innovation.