Physical AI

# NVIDIA Omniverse

Libraries and microservices for developing industrial digital twins and physical AI simulation applications.

[Start Developing](https://developer.nvidia.com/omniverse)

Overview

## What is NVIDIA Omniverse?

NVIDIA Omniverse™ is a collection of libraries and microservices for developing [physical AI](https://www.nvidia.com/en-us/glossary/generative-physical-ai.md) applications such as industrial digital twins and robotics simulation.

Leveraging NVIDIA’s deep expertise in accelerated computing and AI, Omniverse libraries enable software makers to integrate pre-built functionality into their solutions.

### NVIDIA and Industrial Software Giants Bring Design, Engineering, and Manufacturing Into the AI Era

Cadence, Dassault Systèmes, PTC, Siemens, and Synopsys are bringing NVIDIA-accelerated industrial software and tools to major cloud providers and original equipment manufacturers.

[Read Press Release](https://nvidianews.nvidia.com/news/nvidia-and-global-industrial-software-giants-bring-design-engineering-and-manufacturing-into-the-ai-era)

### NVIDIA and Global Robotics Leaders Take Physical AI to the Real World

NVIDIA is partnering with the global robotics ecosystem — including leading robot brain developers, industrial robot giants and humanoid pioneers — to power production-scale physical AI.

[Read Press Release](https://nvidianews.nvidia.com/news/nvidia-and-global-robotics-leaders-take-physical-ai-to-the-real-world)

Technology

## Omniverse Capabilities

[NVIDIA Omniverse libraries](https://developer.nvidia.com/omniverse), microservices, and APIs are built on top of OpenUSD to simplify the adoption of NVIDIA’s physical AI simulation technologies across data interoperability, physics, and rendering.

### OpenUSD

Universal Scene Description ([OpenUSD](https://www.nvidia.com/en-us/glossary/openusd.md)) enables seamless data interoperability and [SimReady](https://www.nvidia.com/en-us/glossary/simready.md) (simulation-ready) digital twins.

[Learn More About OpenUSD](https://developer.nvidia.com/usd)

### RTX

Sensor simulation and physically-based, real-time rendering libraries built on [NVIDIA RTX™](https://www.nvidia.com/en-us/products/workstations/rendering.md) for generating datasets at scale.

[Learn More About Sensor RTX](https://blogs.nvidia.com/blog/omniverse-sensor-rtx-autonomous-machines/)

[Explore ovrtx Library](https://github.com/nvidia-omniverse/ovrtx)

### Physics

GPU-accelerated physics libraries, including [NVIDIA PhysX®](https://developer.nvidia.com/physx-sdk) and [NVIDIA Warp](https://developer.nvidia.com/warp-python), enable scalable simulation and modeling.

[Explore ovphysx Library](https://github.com/NVIDIA-Omniverse/PhysX/tree/main/ovphysx)

### Runtime

Optimized data architecture and runtime for faster development, performance, and collaboration.

[Explore Omniverse Docs](https://docs.nvidia.com/omniverse/index.html)

Use Cases

## How Omniverse Libraries Are Being Used

See how developers across industries are integrating Omniverse libraries into their solutions.

1. Industrial Facility Digital Twins
2. Synthetic Data Generation
3. Robot   
   Simulation
4. Autonomous Vehicle Simulation
5. Robot   
   Learning

### Industrial Facility Digital Twins

Leverage Omniverse libraries to develop advanced virtual factory solutions and bring data interoperability, physically based visualization, generative AI, and real-time collaboration to your software.

[Explore the Industrial Facility Digital Twins Use Case](https://www.nvidia.com/en-us/use-cases/ai-for-virtual-factory-solutions.md)

Delta Electronics

### Synthetic Data Generation

Developers can save significant training time and reduce costs by using synthetic data alongside real-world data to create carefully labeled datasets for training multimodal physical AI models. And now, with [NVIDIA Cosmos](https://www.nvidia.com/en-us/ai/cosmos.md)™, developers can generate even larger [datasets](https://huggingface.co/collections/nvidia/physical-ai) with 3D-to-real workflows.

[Explore the Synthetic Data Generation Use Case](https://www.nvidia.com/en-us/use-cases/synthetic-data.md)

### Robot Simulation

Physical AI-powered robots and robot fleets must autonomously sense, plan, and execute complex tasks in the physical world. These include safely and efficiently transporting and manipulating objects in dynamic, unpredictable environments.

[Explore the Robot Simulation Use Case](https://www.nvidia.com/en-us/use-cases/robotics-simulation.md)

### Autonomous Vehicle Simulation

With [NVIDIA Cosmos](https://www.nvidia.com/en-us/ai/cosmos.md), conditioned on Omniverse physics libraries, simulation developers can enhance their AV simulation workflows with high-fidelity, diverse sensor data and realistic behavior to train perception models and validate the AV software stack.

[Explore the AV Simulation Use Case](https://www.nvidia.com/en-us/use-cases/autonomous-vehicle-simulation.md)

### Robot Learning

Preprogrammed robots struggle with unexpected changes, while AI-driven robots use simulation-based learning to adapt to dynamic environments. This enables them to refine capabilities such as navigation and manipulation, improving performance in a wide range of scenarios.

[Explore the Robot Learning Use Case](https://www.nvidia.com/en-us/use-cases/robot-learning.md)

Agility, Apptronik, Fourier Intelligence, Unitree

[Explore All Use Cases](https://www.nvidia.com/en-us/use-cases.md)

Starting Options

## Ways to Get Started With NVIDIA Omniverse

### Libraries

Integrate Omniverse libraries into physical AI applications.

[Access Libraries](https://developer.nvidia.com/omniverse)

### Blueprints

Jump-start building physical AI solutions with NVIDIA Blueprints.

[Try the Blueprints](https://build.nvidia.com/explore/simulation)

Success Stories

## Transforming Every Industry With NVIDIA Omniverse

[More Success Stories](https://www.nvidia.com/en-us/case-studies.md)

[Robotics

### Skild AI: Pioneering Omni-Bodied Intelligence Through Simulation

**Customer:** Skild AI  
 **Products:** NVIDIA Isaac, NVIDIA Omniverse](https://www.nvidia.com/en-us/case-studies/skild-ai.md)

[Manufacturing

### Lightwheel Accelerates Physical AI Development With NVIDIA Simulation and Foundation Models

**Customer:** AgiBot, BYD, ByteDance, Figure, Fourier, Galbot, Geely, Google Deepmind, Zordi  
 **Products:** NVIDIA Isaac, NVIDIA Omniverse](https://www.nvidia.com/en-us/case-studies/lightwheel.md)

[Manufacturing

### Siemens Accelerates Product Development and Innovation With Industrial AI

**Customer:** BMW Group, HD Hyundai, Maserati  
 **Products:** NVIDIA Omniverse, NVIDIA Metropolis, NVIDIA AI Blueprint for Video Search and Summarization, NeMo Retriever, NIM](https://www.nvidia.com/en-us/case-studies/siemens-accelerates-product-development-and-innovation-with-industrial-ai.md)

Ecosystem

## Industry Leaders Adopt Omniverse

Resources

## The Latest From Omniverse

1. Blogs
2. Sessions
3. Training
4. Videos

### Omniverse News

[See All Tech Blogs](https://developer.nvidia.com/blog/tag/omniverse/)
[See All Topic News](https://blogs.nvidia.com/blog/tag/omniverse/)

Load More

[View All Sessions](https://www.nvidia.com/en-us/on-demand/playlist/playList-44408ff1-cbb9-4280-96eb-945d6451afa5/)

### OpenUSD Learning Path

Gain foundational knowledge, explore essential concepts, and harness the full potential of USD today with our Learn OpenUSD curriculum for developers and 3D practitioners.

[View Learning Path](https://www.nvidia.com/en-us/learn/learning-path/openusd.md)

### Digital Twin Learning Path

Learn the basics of building intelligent factories, warehouses, and industrial facilities with 3D integration, simulation, and real-time visualization for the era of physical AI.

[View Learning Path](https://www.nvidia.com/en-us/learn/learning-path/digital-twins.md)

### Robotics Learning Path

Explore core robotics concepts such as simulation, ROS, and AI training, and how they enable robots to navigate, adapt, and perform tasks in real-world environments.

[View Learning Path](https://www.nvidia.com/en-us/learn/learning-path/robotics.md)

[View All Videos](https://www.nvidia.com/en-us/on-demand/playlist/playList-da1f3d1c-6887-4952-9715-c48bf50e51f6/)

Next Steps

## Stay Up to Date on NVIDIA Omniverse News

Get the latest news, breakthroughs, and more sent straight to your inbox.

[Stay Informed](https://www.nvidia.com/en-us/omniverse/news.md)

## Frequently Asked Questions

### What is NVIDIA Omniverse?

NVIDIA Omniverse™ is a collection of libraries and microservices for developing physical AI applications such as industrial digital twins and robotics simulation. Leveraging NVIDIA’s deep expertise in accelerated computing and AI, Omniverse libraries enable software makers to integrate pre-built functionality into their solutions. These libraries include developer tools, GPU-accelerated libraries, and technologies packaged as microservices and cloud APIs for streamlined development and deployment.

### How can I develop on NVIDIA Omniverse?

There are two ways to start developing with NVIDIA Omniverse libraries:

1. Leverage NVIDIA Omniverse libraries within your physical AI application for direct integration of prebuilt functionality, including sensor simulation, physically based real-time rendering, and advanced physics.
2. Jump-start building physical AI solutions with NVIDIA Blueprints, comprehensive reference projects and workflows that use NVIDIA AI and Omniverse technologies to help developers build and customize end-to-end applications such as industrial digital twins and robotics simulations.

Learn more about developing with NVIDIA Omniverse libraries [here](https://developer.nvidia.com/omniverse).

### Where can I access legacy tools like Omniverse Launcher?

**NVIDIA Omniverse Launcher (Deprecated)**

The Omniverse Launcher was deprecated on October 1, 2025 to better align with developers and their expected development workflows.

Many of the Omniverse applications, tools, and assets that used to live in Launcher will transition to the following locations:

* Kit, Apps, Samples, Tools, and Templates are available on [GitHub](https://github.com/NVIDIA-Omniverse) and the [NGC Catalog](https://catalog.ngc.nvidia.com/collections?filters=platform%7COmniverse%7Cpltfm_omniverse&orderBy=weightPopularDESC&query=&page=&pageSize=).
* Connectors are available in the [NGC Catalog](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/omniverse/collections/omni_connectors).
* Extensions are available directly from the associated vendor's web pages or [NVIDIA OpenUSD Ecosystem Catalog](https://www.nvidia.com/en-us/accelerated-applications/usd-ecosystem.md).
* Content and Assets are available on [NVIDIA Documentation Hub (OmniDocs)](https://docs.omniverse.nvidia.com/).

Visit [Omniverse legacy tools](http://developer.nvidia.com/omniverse/legacy-tools) and [NVIDIA developer forums](https://forums.developer.nvidia.com/c/omniverse/platform/launcher/401) for more details.

**Nucleus Workstation**  
 Nucleus Workstation on Launcher was deprecated on October 1, 2025. Developers wishing to continue using Nucleus can obtain the Enterprise Nucleus Server software from the NGC Catalog (login required). The Enterprise Nucleus Server is free for testing and development, but an enterprise license is required for production use and includes enterprise support. See [Omniverse legacy tools](http://developer.nvidia.com/omniverse/legacy-tools) for more details.

### What is NVIDIA Cosmos, and how is it different from Omniverse?

[NVIDIA Cosmos](https://www.nvidia.com/en-us/ai/cosmos.md) is a [world model (WFM)](https://www.nvidia.com/en-us/glossary/world-models.md) development platform. At its core are Cosmos WFMs that generate world states as videos using multimodal input.

Developers can input Omniverse simulations as instructional videos to [the Cosmos Transfer WFM](https://github.com/nvidia-cosmos/cosmos-transfer2.5) model to generate controllable, photorealistic synthetic data.

Together, Omniverse provides the simulation environment before and after training, while Cosmos photoreal controllable synthetic data to train physical AI models.