What Is SimReady?

Simulation-ready assets follow paradigms to solve the biggest challenges for organizations building digital twins, robotics, and AI-driven solutions, including speed, scale, reliability, and cross-platform compatibility. By adopting simulation-ready content, teams accelerate product development, reduce deployment risk, and unlock new levels of operational efficiency, making advanced simulation and intelligent automation accessible to every industry.

Real-World Accuracy: Simulation-ready assets are photoreal and physically accurate to reflect real-world behavior in simulated environments, such as digital twins.

Flexibility and Modularity: Simulation-ready assets take advantage of the modular and flexible nature of USD, enabling developers to integrate 3D assets into any workflow or virtual world.

Consistency: 3D developers working on digital twins have consistency across their asset libraries and can quickly build virtual worlds.

Scalability: Simulation-ready assets enable large-scale virtual training and testing—accelerating physical AI development across robotics, manufacturing, and other industries.

Future-Proof Workflow: Using simulation-ready assets means your 3D library stays interoperable, adaptable, and upgradeable as technology, platforms, and business needs evolve.

Simulation-ready eliminates ambiguity and enables ecosystems to interoperate and innovate at scale.

Simulation-ready assets are full-fidelity digital objects built on OpenUSD. These digital assets represent geometry, physics, appearances, and annotations and will continually evolve to encompass all features needed to accurately represent and simulate real-world objects.

When a simulation-ready asset is loaded into a framework like NVIDIA Isaac Sim™, simulation runtimes consume rich contextual data, available through object representation in OpenUSD, to power various modalities, including physics, AI, and sensor models. This enables realistic asset behavior, such as accurate simulations of a robotic arm grasping an object.

By leveraging a unified framework for developing and standardizing content capabilities and compliance, these assets can be modified, extended, or reused across different simulation runtimes and projects. They're essential for creating physically accurate digital twins and generating high-quality synthetic training data for physical AI and machine learning. This process allows developers to test and validate autonomous systems in a safe, scalable virtual environment before real-world deployment.

Simulation-ready asset creation involves capturing the geometry, appearance, and real-world physical behaviors of objects, then structuring this digital representation to include all necessary simulation inputs. This incorporates many best practices for conceptual data mapping and asset structuring in OpenUSD.

Simulation-ready assets include features that enable robust interaction in synthetic environments, such as:

• Semantic Labeling: Provide ground truth for machine learning by verifying perception system identification and classification of objects.
• Non-Visual Sensor and Non-Visual Material Attributes: Enable accurate non-visual sensor simulation (such as radar, lidar, thermal imaging) with attributes including material properties that affect sensor response but aren't visible to the human eye.
• Collision Shapes: Define physical boundaries that determine how objects interact and collide within the simulation environment.
• Mass Properties: Include weight, center of mass, and inertia data that enable realistic physics calculations during simulation.
• Material Details: Specify surface characteristics like friction, elasticity, and visual properties that affect object behavior and appearance.

The features listed represent common examples, but further properties may be available depending on asset type.

Developers can compose, configure, and deploy simulation-ready assets in various simulation scenarios—including industrial automation and robotics. Once validated, SimReady assets can also serve as ground-truth models for training AI agents in robotics, autonomous vehicle perception, and manufacturing optimization.

To optimize the design, simulation, deployment, and operations of AI factories, SimReady assets can be used to populate a digital twin of the AI factory with 3D models of power, cooling, and mechanical systems.

Simulation-ready assets enable AI and robotics training by providing standardized inputs for simulations that replicate real-world complexity without real-world risks. Digital twins and photorealistic environments support training, testing, and validation of AI systems at scale, accelerating iteration and experimentation.

Asset behavior (such as sensor simulation) can be configured to represent a rich set of simulation scenarios to support reinforcement learning, supervised learning, and synthetic data generation for robotics and autonomous systems.

Once models are adequately trained in simulation, their knowledge and policies can be transferred to physical robots or systems for real-world testing and deployment.

By using SimReady assets, developers can design frameworks and benchmarks where robot performance is controlled and repeatable.

Simulation readiness is the foundation for building physically accurate digital twins. By providing access to real-world properties and behaviors from the digital asset, simulation-ready assets maximize the applicability of 3D content to robotics, manufacturing, healthcare, and autonomous systems.

Industrial Automation and Robotics

Building realistic industrial simulations requires thousands of 3D assets. Simulation-ready assets accelerate development and testing of AI-driven robots and other agents in industrial settings by providing reliable, physically accurate 3D models for simulation scenarios.

Digital Twin Development

Simulation-ready assets allow teams to create digital twins of warehouses, factories, data centers, and smart spaces by supplying standardized 3D models—like forklifts, racks, and conveyors—that accurately simulate workflows, resource movement, and robot interactions. This enables optimization of layouts and productivity and supports intelligent robotics training, all before deploying changes in the real world.

Autonomous Vehicles

Training autonomous systems requires highly realistic, varied environments. Simulation-ready assets provide labeled, photorealistic street objects, vehicles, pedestrians, and virtual hazard scenarios, accelerating sensor validation and AI model training.

Healthcare and Smart Cities  

Digital twins of hospitals, retail spaces, and urban infrastructure can improve safety analysis, process management, and efficiency planning. Simulation-ready assets support the creation of complex environments for safety planning and efficiency analysis in hospitals and urban infrastructure.

Simulation-ready development and standardization creates the foundation for simulation-ready 3D assets, enabling interoperable digital content for digital twins, robotics, and AI training. This evolving framework ensures virtual assets are not only visually accurate but also functionally consistent through three key principles:

1. Feature Expansion: Expanding the capabilities of simulation-ready assets by incorporating more sophisticated features—like advanced physics behaviors and articulated parts—to support complex and realistic simulations.
2. Pipeline Scaling: Automating the conversion and validation of a high volume of source assets into a simulation-ready OpenUSD to enable efficient production for large-scale digital environments and training datasets.
3. Standardization Review: Defining how simulation-ready features are represented within OpenUSD to ensure they're universally supported across the ecosystem through a growing number of compatible tools and platforms.

Simulation-ready standardization faces key challenges in a rapidly evolving technological landscape. The following must be addressed to ensure the framework’s long-term viability:

1. Evolving Scope With Technology: Rapid technological change makes it difficult to define an appropriate and enduring scope for simulation-ready features and pipeline workflows.
2. Industry-Specific Needs: Different industries have distinct requirements, priorities, and conventions. Ongoing standardization demands broad cross-industry collaboration to reduce ambiguity and ensure relevance.
3. Integration Across Simulation Runtimes: Maintaining compatibility across diverse simulation frameworks and tools, as well as downstream applications, becomes more complex as industries and simulation solutions continue to evolve.

The SimReady Standardization Workflow helps mitigate these challenges. This collaborative, phased process ensures the continuous iteration and refinement of standards, keeping the framework relevant and adaptable to future technological advancements and industry needs, as detailed in the documentation.

Leverage the tools, standards, and resources below to begin using and creating simulation-ready assets.

• Explore assets in Isaac Sim: Start by downloading assets from NVIDIA’s physical AI dataset or Lightwheel’s extensive library, then try them out within NVIDIA Isaac Sim.
• Follow the standardization workflow: Build and validate asset capabilities for your simulation projects step-by-step using the SimReady Standardization Workflow.
• Get OpenUSD certified: Validate your expertise in building 3D content pipelines by earning the NVIDIA-Certified Professional OpenUSD Development certification.
• Contribute to the ecosystem: Use your domain expertise to contribute to the ongoing development of OpenUSD and simulation-ready standards through the Alliance for OpenUSD (AOUSD).