The OpenUSD 3D Framework Is Transforming Automation Design

May 12, 2026

Lauren Gibbons Paul

Image Source: MangKangMangMee/stock.adobe.com

By Lauren Gibbons Paul for Mouser Electronics

Published May 12, 2026

Automation systems involve many different tools and data sources, from robot models and control software to simulation and layout tools. Keeping those pieces aligned has become increasingly difficult, especially when they come from different vendors. Most existing engineering tools were never designed to share data easily at this level. Open Universal Scene Description (OpenUSD) addresses that gap by providing a common way to represent and exchange system information.

OpenUSD was not originally designed for manufacturing. Pixar Animation Studios developed the framework to manage large, complex 3D scenes in which many contributors needed to work simultaneously.^[1] Instead of treating a scene as a single file, OpenUSD handles many assets at once, with changes layered on top of one another rather than permanently baked in.

That design applies well to industrial digital twins. Automation systems consist of many moving parts that can evolve over time. OpenUSD makes it possible to update individual elements without breaking the larger system, a key requirement for modeling and maintaining digital representations of real production environments.^[2]

The technology has evolved far beyond its cinematic roots. OpenUSD is used as the underlying data layer in NVIDIA Omniverse, enabling manufacturers to develop, visualize, and coordinate industrial digital twin models across multiple simulation and design tools.^[3]

OpenUSD is particularly powerful for automation because of its vendor-neutral interoperability. A single USD scene can combine everything from computer-aided design (CAD) geometry, robot kinematics, and sensor models to programmable logic controller (PLC) logic references, physics simulations, and scene metadata into a single coherent representation. Engineers can move between tools and collaborate across organizations without being locked into a single format.

That momentum led to the formation of the Alliance for OpenUSD (AOUSD) by Pixar, NVIDIA, Adobe, Apple, and Autodesk. The alliance strives to standardize OpenUSD as a common 3D data framework across industries, as HTML standardized the web.^[4] The result is a platform that enables manufacturers to collaborate on automation design. Teams can work from the same models across design and simulation, even when they use different tools.

OpenUSD’s initial purpose was for complex 3D collaboration, but now it fits naturally into industrial workflows. Automation providers are using it to bring robot models, control-driven equipment, vision systems, and safety elements into a single simulation environment. Many platforms rely on OpenUSD as their underlying data layer, enabling engineers to design and validate automation workcells virtually before hardware is installed. The same environments are also being used to support robotics development and artificial intelligence (AI) training, where realistic system behavior is required before physical systems are available.

How Automation Providers Are Using OpenUSD

Automation providers are using OpenUSD to work with system data across design and simulation tools. Because the same model can move through each development stage, teams can carry system information forward as an automation project takes shape and is eventually deployed.

Validating Logic Before Deployment

In the past, engineers often did not know if the automation software really worked until the machines were physically installed and powered up in the factory. By using USD-based digital twins, they can perform much of that validation earlier in the process. Engineers can evaluate how robots move, including safety interactions and clearances, in a virtual environment, which helps them identify problems before equipment is installed.

Collaborating Across Engineering Teams

OpenUSD’s nondestructive layering allows for mechanical, electrical, controls, and software teams to work together, simultaneously. Each team of engineers can contribute changes without overwriting others’ work.

Creating AI Training Environments

Engineers can use virtual models of workcells to generate AI training data and test behavior under different conditions, without changing the underlying system model.

Scaling Modular Workcells

As automation systems become more modular, teams are looking for ways to reuse proven workcell designs. OpenUSD makes this easier by allowing existing workcell models to be copied and adjusted for new layouts, rather than having teams rebuild from scratch.

Integrating Physics-Based Simulation

OpenUSD allows engineers to move beyond static models by describing motion, kinematics, and physical properties within a shared scene. While USD itself does not perform physics simulation, it can encode time-based transforms, articulated joints, collision geometry, and material properties in a standardized way.

These definitions can then be consumed by physics and simulation engines to evaluate how a system behaves when moving at speed, interacting with other components, or handling real-world loads. This separation allows teams to maintain a single, authoritative scene description in OpenUSD while leveraging specialized solvers to simulate realistic physical behavior.

Siemens and NVIDIA Omniverse

One example of OpenUSD’s industrial role is the collaboration between Siemens and NVIDIA, which uses OpenUSD within NVIDIA Omniverse to create composable digital twins of robotic workcells. Siemens has integrated its automation and simulation tools within Omniverse to provide a unified, USD-based representation of industrial systems.^[5]

A typical robotic workcell modeled in this environment may include:

An industrial robot
Safety scanners and light curtains
A PLC-controlled conveyor system
Machine-vision cameras
End-of-arm tooling (e.g., grippers, welders, pick-and-place heads)
Guarding, fixtures, and material flow paths

Each of these components is represented as a USD asset that includes geometry, kinematic constraints, metadata, and references to control logic. Engineers can combine assets like robot CAD models, vision system configurations, conveyor layouts, and PLC data into a single USD scene.

With Siemens Process Simulate, robot paths and workcell dynamics are integrated as USD layers. NVIDIA Omniverse then merges those layers into a coherent digital twin, while PLC logic runs on external controllers and is co-simulated to validate the full behavior of the workcell.^[6]

Working from a live digital model instead of disconnected files changes how teams can evaluate a system before anything is built. For example, collisions and clearance issues will show up early. Even cycle times and throughput can be evaluated before hardware is ordered. Engineers can see whether a robot can reach its targets, whether a specific tooling choice makes sense, and whether PLC sequencing behaves as expected. The model makes it easier to see how a system behaves before deployment, rather than having operators discover problems during commissioning.

Once the workcell is validated virtually, the same USD-based digital twin becomes the baseline for ongoing operations. Sensor data and system feedback can be reapplied to the model, which helps operators understand what is working and what changes are needed.

NVIDIA uses OpenUSD as the core scene description framework, enabling AI training environments and operational digital twins to share consistent geometry, semantics, and metadata across simulation and deployment workflows.^[7]

How OpenUSD Unifies Robots, Controls, and Sensors

OpenUSD provides a shared data layer that ties together many automation components, enabling engineers to design and validate the system as a whole, rather than modeling each component in isolation. In a typical USD-based industrial digital twin, a system includes the following:

Industrial robots and cobots: Robot geometry, joint limits, kinematics, and motion paths can be imported as USD assets. This supports reachability checks and collision analysis within full workcell simulations.

Robot controllers and motion logic: Controller-level logic, motion sequencing, and timing data can be referenced within USD scenes, enabling physical movement and control behavior to be synchronized during simulation. Engineers can evaluate how physical movement aligns with control behavior during simulation.

Machine vision systems: Cameras, lighting, lenses, and fields of view can be modeled directly in OpenUSD environments.

PLCs and industrial control systems: PLC-driven conveyors, actuators, and safety logic can be represented alongside mechanical assets, which makes it easier for engineers to validate control sequences and material flows digitally before deployment.

Safety devices: Users can model light curtains, safety scanners, emergency stops, and guarding with their effective detection zones, enabling them to review safety behavior early in the design process.

End effectors and tooling: Grippers, weld guns, screwdrivers, and pick-and-place tools can be treated as modular USD assets, which helps teams evaluate tooling changes and task flexibility without redesigning the entire system.

Industrial networking and data flow: Metadata within USD scenes can reference industrial communication layers such as PROFINET, Ethernet/IP, and OPC UA, helping teams align physical layout with control and data architectures.

When these elements are combined, they give engineers insight into how an automation system is put together and how it behaves when conditions change.

Conclusion

OpenUSD gives automation providers a practical way to share and reuse 3D system data across tools and teams. By working from a common scene representation, engineers can design, simulate, and validate automation systems with fewer handoffs and less rework during commissioning.

With increasing vendor support, OpenUSD is becoming foundational in industrial simulation environments. As automation ecosystems become more complex and multiple manufacturers work more closely together, OpenUSD defines a future in which many vendors can design, assemble, and digitally optimize a system before a single bolt is tightened.