Into the Omniverse: NVIDIA GTC Showcases Virtual Worlds Powering the Physical AI Era

<div id="bsf_rt_marker"></div><p><i><span style="font-weight: 400">Editor’s note: This post is part of </span></i><a target="_blank" href="https://www.nvidia.com/en-us/omniverse/news/"><i><span style="font-weight: 400">Into the Omniverse</span></i></a><i><span style="font-weight: 400">, a series focused on how developers, 3D practitioners, and enterprises can transform their workflows using the latest advances in </span></i><a target="_blank" href="https://www.nvidia.com/en-us/omniverse/usd/"><i><span style="font-weight: 400">OpenUSD</span></i></a><i><span style="font-weight: 400"> and </span></i><a target="_blank" href="https://www.nvidia.com/en-us/omniverse/usd/"><i><span style="font-weight: 400">NVIDIA Omniverse</span></i></a><i><span style="font-weight: 400">.</span></i></p> <p><span style="font-weight: 400">NVIDIA GTC last week showcased a turning point in </span><a target="_blank" href="https://www.nvidia.com/en-us/glossary/generative-physical-ai/"><span style="font-weight: 400">physical AI</span></a><span style="font-weight: 400">: Robots, vehicles and factories are scaling from single use cases and isolated deployments to sophisticated enterprise workloads across industries. </span></p> <p><span style="font-weight: 400">At the center of this shift are new frontier models for physical AI, including NVIDIA Cosmos 3, NVIDIA Isaac GR00T N1.7 and NVIDIA Alpamayo 1.5. </span></p> <p><span style="font-weight: 400">NVIDIA also released the </span><a target="_blank" href="https://nvidianews.nvidia.com/news/nvidia-announces-open-physical-ai-data-factory-blueprint-to-accelerate-robotics-vision-ai-agents-and-autonomous-vehicle-development"><span style="font-weight: 400">NVIDIA Physical AI Data Factory Blueprint</span></a><span style="font-weight: 400">, designed to push the state of the art in world modeling, humanoid skills and autonomous driving, as well as the </span><a target="_blank" href="https://nvidianews.nvidia.com/news/nvidia-releases-vera-rubin-dsx-ai-factory-reference-design-and-omniverse-dsx-digital-twin-blueprint-with-broad-industry-support"><span style="font-weight: 400">NVIDIA Omniverse DSX Blueprint</span></a><span style="font-weight: 400"> for AI factory digital twin simulation.</span></p> <p><span style="font-weight: 400">Open source agentic frameworks such as OpenClaw extend the AI stack all the way to operations — enabling long‑running “claws” that use tools, memory and messaging interfaces to orchestrate workflows, manage data pipelines and execute tasks autonomously on dedicated machines. </span></p> <p><span style="font-weight: 400">“With NVIDIA and the broader ecosystem, we’re building the claws and guardrails that let anyone create powerful, secure AI assistants,” said Peter Steinberger, creator of OpenClaw, in an </span><a target="_blank" href="https://nvidianews.nvidia.com/news/nvidia-announces-nemoclaw"><span style="font-weight: 400">NVIDIA press release</span></a><span style="font-weight: 400"> from GTC. </span></p> <p><a target="_blank" href="https://www.nvidia.com/en-us/omniverse/usd/"><span style="font-weight: 400">OpenUSD</span></a><span style="font-weight: 400"> is a driving force behind the scalability of physical AI — providing a common, scene‑description language that lets teams bring computer-aided design (CAD) data, simulation assets and real‑world telemetry into a shared, physically accurate view of the world. </span></p> <h2><b>Simulating the AI Factory Before It’s Built</b></h2> <p><span style="font-weight: 400">Modern AI factories are complex — spanning thermals, power grids, network load and mechanical systems. Building them on time and on budget becomes much easier when using simulation technology. </span></p> <p><span style="font-weight: 400">To tackle this, NVIDIA introduced the </span><a target="_blank" href="https://nvidianews.nvidia.com/news/nvidia-releases-vera-rubin-dsx-ai-factory-reference-design-and-omniverse-dsx-digital-twin-blueprint-with-broad-industry-support"><span style="font-weight: 400">Omniverse DSX Blueprint</span></a><span style="font-weight: 400"> at GTC, a reference architecture that unifies simulation across every layer of an AI factory through a single digital twin. This enables operators to optimize performance and efficiency before a rack is installed in the real world.</span></p> <h2><b>Compute Is Data: Real-World Data Is No Longer the Moat</b></h2> <p><span style="font-weight: 400">Real-world data used to function as a moat for physical AI — but it doesn’t scale. The real world is messy, unpredictable and full of edge cases, and the pipelines to process, simulate and evaluate data are fragmented. The bottleneck isn’t just data — it’s the entire data factory.</span></p> <p><span style="font-weight: 400">To help address this, NVIDIA introduced at GTC its </span><a target="_blank" href="https://nvidianews.nvidia.com/news/nvidia-announces-open-physical-ai-data-factory-blueprint-to-accelerate-robotics-vision-ai-agents-and-autonomous-vehicle-development"><span style="font-weight: 400">Physical AI Data Factory Blueprint</span></a><span style="font-weight: 400">, an open reference architecture that transforms compute into large-scale, high-quality training data. Built on </span><a target="_blank" href="https://www.nvidia.com/en-us/ai/cosmos/"><span style="font-weight: 400">NVIDIA Cosmos open world foundation models</span></a><span style="font-weight: 400"> and the </span><a target="_blank" href="https://developer.nvidia.com/osmo"><span style="font-weight: 400">NVIDIA OSMO</span></a><span style="font-weight: 400"> operator, it unifies data curation, augmentation and evaluation into a single pipeline, enabling developers to generate diverse, long-tail datasets from limited real-world inputs.</span></p> <p><span style="font-weight: 400">Leading physical AI developers including </span><a target="_blank" href="https://www.fieldai.com/news/fieldai-accelerates-industrial-customers-adoption-of-ai-in-collaboration-with-nvidia"><span style="font-weight: 400">FieldAI</span></a><span style="font-weight: 400">, </span><a target="_blank" href="https://robotics.hexagon.com/industrial-autonomy-hexagon-robotics-nvidia-physical-ai/"><span style="font-weight: 400">Hexagon Robotics</span></a><span style="font-weight: 400">, </span><a target="_blank" href="https://www.prnewswire.com/news-releases/linker-vision-highlights-video-reasoning-ai-at-nvidia-gtc-2026-302714380.html"><span style="font-weight: 400">Linker Vision</span></a><span style="font-weight: 400">, </span><a target="_blank" href="https://www.milestonesys.com/company/news/press-releases/ai-as-a-service-at-nvidia-gtc/"><span style="font-weight: 400">Milestone Systems</span></a><span style="font-weight: 400">, </span><a target="_blank" href="https://www.skild.ai/blogs/reindustrial-revolution"><span style="font-weight: 400">Skild AI</span></a><span style="font-weight: 400"> and </span><a target="_blank" href="https://www.universal-robots.com/news-and-media/news-center/universal-robots-scale-ai-launch-imitation-learning-system-accelerate-ai-training-lab-to-factory/"><span style="font-weight: 400">Teradyne Robotics</span></a><span style="font-weight: 400"> are already tapping the blueprint to speed up robotics projects, vision AI agents and autonomous vehicle programs.</span></p> <p><a target="_blank" href="https://azure.microsoft.com/en-us"><span style="font-weight: 400">Microsoft Azure</span></a><span style="font-weight: 400"> and </span><a target="_blank" href="https://nebius.com/?utm_term=nebius&amp;utm_campaign=Search_us_all_lgen_brand_cloud&amp;utm_source=google&amp;utm_medium=cpc&amp;hsa_acc=3900112445&amp;hsa_cam=21863834149&amp;hsa_grp=173545027150&amp;hsa_ad=797256119441&amp;hsa_src=g&amp;hsa_tgt=kwd-1496427731642&amp;hsa_kw=nebius&amp;hsa_mt=p&amp;hsa_net=adwords&amp;hsa_ver=3&amp;gad_source=1&amp;gad_campaignid=21863834149&amp;gclid=CjwKCAjwpcTNBhA5EiwAdO1S9mCKsrmaSvioZDA4wzAMNJkHJBWbEAeP7HMRpipjp7IGccKlvvbOOhoCaV8QAvD_BwE"><span style="font-weight: 400">Nebius</span></a><span style="font-weight: 400"> are the first cloud platforms to offer the blueprint, turning world-scale compute into turnkey data production engines.</span></p> <p><span style="font-weight: 400">“Together with cloud leaders, we’re providing a new kind of agentic engine that transforms compute into the high-quality data required to bring the next generation of autonomous systems and robots to life,” said Rev Lebaredian, vice president of Omniverse and simulation technologies at NVIDIA, in </span><a target="_blank" href="https://nvidianews.nvidia.com/news/nvidia-announces-open-physical-ai-data-factory-blueprint-to-accelerate-robotics-vision-ai-agents-and-autonomous-vehicle-development"><span style="font-weight: 400">this press release</span></a><span style="font-weight: 400">. “In this new era, compute is data.”</span></p> <h2><b>From OpenUSD to Reality: Seamless Design to Deployment</b></h2> <p><span style="font-weight: 400">Converting CAD files to </span><a target="_blank" href="https://docs.nvidia.com/learn-openusd/latest/glossary.html"><span style="font-weight: 400">OpenUSD</span></a><span style="font-weight: 400"> is a critical step in the physical AI pipeline — transforming engineering data into simulation-ready assets that developers can use to build, test and validate robots in physically accurate virtual environments. </span></p> <p><span style="font-weight: 400">Using tools like the </span><a target="_blank" href="https://docs.omniverse.nvidia.com/kit/docs/kit-app-template/latest/docs/kit_sdk_overview.html"><span style="font-weight: 400">NVIDIA Omniverse Kit</span></a><span style="font-weight: 400"> software development kit and </span><a target="_blank" href="https://developer.nvidia.com/isaac/sim"><span style="font-weight: 400">NVIDIA Isaac Sim</span></a><span style="font-weight: 400">, teams can optimize and enrich 3D data for real-time rendering, simulation and collaborative workflows.  </span></p> <p><span style="font-weight: 400">Companies including </span><a target="_blank" href="https://www.fanucamerica.com/news-resources/fanuc-america-press-releases/2026/03/16/fanuc-accelerates-physical-ai-in-industrial-robotics-leveraging-nvidia-technologies"><span style="font-weight: 400">FANUC</span></a><span style="font-weight: 400"> and Fauna Robotics are using this seamless CAD-to-OpenUSD workflow to speed up robotic system design and validation.</span></p> <h2><b>Transforming Manufacturing and Logistics Through Industrial Digital Twins</b></h2> <p><span style="font-weight: 400">“Factories themselves are now robotic systems,” Lebaredian said during his special address on digital twins and simulation at GTC. </span></p> <p><span style="font-weight: 400">All factories are born in simulation. The </span><a target="_blank" href="https://www.nvidia.com/en-us/industries/manufacturing/mega-blueprint/"><span style="font-weight: 400">NVIDIA Mega Omniverse Blueprint</span></a><span style="font-weight: 400"> provides enterprises with a reference architecture to design, test and optimize robot fleets and AI agents in a physically accurate facility digital twin before a single robot is deployed on the floor. </span></p> <p><a target="_blank" href="https://www.kiongroup.com/en/News-Stories/Press-Releases/Press-Releases-Detail.html?id=1099696911&amp;type=corporate&amp;title=KION%20brings%20physical%20AI%20into%20live%20warehouse%20operations%20at%20GTC%202026%20in%20San%20Jos%C3%A9,%20California"><span style="font-weight: 400">KION</span></a><span style="font-weight: 400">, working with Accenture and Siemens, is using this blueprint to build large-scale warehouse digital twins that train and test fleets of NVIDIA Jetson-based autonomous forklifts for GXO, the world’s largest pure-play contract logistics provider. </span></p> <h2><b>Physical AI Steps From Simulation to the Real World</b></h2> <p><iframe loading="lazy" title="Official Keynote Closing Video | GTC 2026" width="1200" height="675" src="https://www.youtube.com/embed/aDT9bBt9HxM?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></p> <p><span style="font-weight: 400">NVIDIA is partnering with the global robotics ecosystem — including leading robot brain developers, industrial robot giants and humanoid pioneers — to enhance production-level physical AI. </span></p> <p><a target="_blank" href="https://new.abb.com/news/detail/134030/prsrl-abb-robotics-partners-with-nvidia-to-deliver-industrial-grade-physical-ai-at-scale"><span style="font-weight: 400">ABB Robotics</span></a><span style="font-weight: 400">, </span><a target="_blank" href="https://www.fanucamerica.com/news-resources/fanuc-america-press-releases/2026/03/16/fanuc-accelerates-physical-ai-in-industrial-robotics-leveraging-nvidia-technologies"><span style="font-weight: 400">FANUC</span></a><span style="font-weight: 400">, </span><a target="_blank" href="https://www.kuka.com/en-de/company/press/news/2026/03/kuka-amp-nvidia-gtc"><span style="font-weight: 400">KUKA</span></a><span style="font-weight: 400"> and Yaskawa, which have a combined global install base of over 2 million robots, are using </span><a target="_blank" href="https://www.nvidia.com/en-us/omniverse/"><span style="font-weight: 400">NVIDIA Omniverse</span></a><span style="font-weight: 400"> libraries and </span><a target="_blank" href="https://developer.nvidia.com/isaac"><span style="font-weight: 400">NVIDIA Isaac</span></a><span style="font-weight: 400"> simulation frameworks to validate complex robot applications and production lines through physically accurate </span><a target="_blank" href="https://www.nvidia.com/en-us/glossary/digital-twin/"><span style="font-weight: 400">digital twins</span></a><span style="font-weight: 400">. These companies have also integrated </span><a target="_blank" href="https://developer.nvidia.com/embedded/jetson-modules"><span style="font-weight: 400">NVIDIA Jetson modules</span></a><span style="font-weight: 400"> into their controllers to enable real-time AI inference. </span></p> <p><span style="font-weight: 400">Robot development starts with the robot brains, which is why leading developers including FieldAI and </span><a target="_blank" href="https://www.nvidia.com/en-us/case-studies/skild-ai/"><span style="font-weight: 400">Skild AI</span></a><span style="font-weight: 400"> are building theirs using </span><a target="_blank" href="https://www.nvidia.com/en-us/ai/cosmos/"><span style="font-weight: 400">NVIDIA Cosmos</span></a><span style="font-weight: 400"> world models for </span><a target="_blank" href="https://www.nvidia.com/en-us/use-cases/synthetic-data/"><span style="font-weight: 400">data generation</span></a><span style="font-weight: 400"> and Isaac simulation frameworks to validate policies in simulation. </span></p> <p><span style="font-weight: 400">Meanwhile, Generalist AI is using NVIDIA Cosmos to explore generating synthetic data. This combination allows robots to become proficient in any task — from supply chain monitoring to food delivery — at an exceptional pace. </span></p> <p><i><span style="font-weight: 400">Read all of NVIDIA’s announcements from GTC on this </span></i><a target="_blank" href="https://nvidianews.nvidia.com/online-press-kit/gtc-2026-news"><i><span style="font-weight: 400">online press kit</span></i></a><i><span style="font-weight: 400"> and </span></i><a target="_blank" href="https://www.youtube.com/watch?v=jw_o0xr8MWU&amp;t=2s"><i><span style="font-weight: 400">watch the keynote replay</span></i></a><i><span style="font-weight: 400">. Catch up on all <a target="_blank" href="https://www.youtube.com/playlist?list=PL3jK4xNnlCVclphegeS4R9JYbhWprKJe_">Physical AI Days</a> sessions from GTC and watch the <a target="_blank" href="https://www.youtube.com/live/MplaRtIZerU?si=Vn-S0BNz4xC-fVjL&amp;t=11130">developer livestream</a> replay. </span></i></p>