3 Easy Ways to Supercharge Your Robotics Development Using OpenUSD

Data ingestion converts various data formats into OpenUSD, serving as a gateway to NVIDIA Isaac Sim and the NVIDIA robotics ecosystem.

Modern robotics projects are built on a posh foundation of disparate data sources, including CAD files, URDF descriptions for robot kinematics, live sensors, and IoT data. OpenUSD acts because the universal aggregator, unifying these sources right into a single, cohesive format.

This unification:

• Enables advanced workflows like synthetic data generation, software in-the-loop testing of robotics algorithms, and reinforcement learning on frameworks similar to Isaac Sim and NVIDIA Isaac Lab.
• Accelerates development by creating a standard USD representation that streamlines your entire pipeline from design to AI training.

Apply it now to your workflows:

Several converters and sensor models are useful for robotics workflows, including:

• Wandlebots OpenUSD library: Wandelbots NOVA includes an intensive library of annotated OpenUSD robot models from manufacturers similar to FANUC, Yaskawa, Universal Robots, ABB, and KUKA.
• SICK virtual sensor models: Access certified digital twins of commercial LiDAR sensors, safety laser scanners, and vision sensors in OpenUSD format, ready for training simulations in Isaac Sim.
• Newton’s MuJoCo-USD Converter: Transform MuJoCo (MJCF) files into OpenUSD with physics, geometry, and material support.

Create a knowledge pipeline to automate your MJCF file conversions to OpenUSD:

pip install mujoco-usd-converter
mujoco_usd_converter /path/to/robot.xml /path/to/usd_robot

2. Data aggregation: scale to massive virtual worlds

Data aggregation uses OpenUSD’s layer-based composition to mix modular, reusable assets from disparate sources into organized, scalable, and performant virtual worlds.

OpenUSD manages a whole lot of hundreds of objects inside single environments, enabling large-scale robotic simulations where fleets of robots train, test, and optimize in realistic scenarios. By constructing modular, reusable assets—like warehouse shelves or factory robots—you create infinite environment configurations. This accelerates AI model training, enhances synthetic data generation diversity, and produces more robust, versatile robot performance in real-world deployments.

Apply it now to your workflows:

• The Physical AI Warehouse OpenUSD Dataset on Hugging Face offers developers a head start with nearly 1,000 OpenUSD assets for warehouse robotics simulation and training.
• USD Search can provide help to manage large amounts of assets and locate what you wish faster (even when the 3D data is unstructured and untagged) using AI-powered natural language or image queries.

Start aggregating assets into countless large virtual environments for training.

You may automate your scene construction by non-destructively referencing assets from the dataset in Python:

from pathlib import Path
from pxr import Usd

def ref_all_dataset_assets(root_dir: Path, stage: Usd.Stage):
    for usd_file in root_path.rglob('*.usd'):
        dir_name = usd_file.parent.name
        file_name = usd_file.stem
        if file_name == dir_name:
            print(f"Found asset entry point: {usd_file}")
            
            # Define a typeless prim
            prim_path = f"/{dir_name}"
            prim = stage.DefinePrim(prim_path)
                
            # Add reference to the layer
            prim.GetReferences().AddReference(str(usd_file))

Learn more concerning the USD Search API.

3. SimReady: unify your robotics pipeline with the broader ecosystem

SimReady assets are high-fidelity OpenUSD objects that incorporate physically accurate properties—materials, kinematics, and behaviors—making them immediately usable for realistic simulation, robotics, AI training, and digital twins.

Using a SimReady asset catalog streamlines your robotics pipeline by avoiding fragmentation and compatibility issues that plague ad-hoc 3D models. This standardization enables asset interoperability, reuse, and integration across simulation runtimes. SimReady assets are immediately usable in OpenUSD-powered frameworks like Isaac Sim, eliminating time-consuming asset preparation and conversion. This permits developers to deal with core value-add activities—training and simulation—while using the appropriate tool for each pipeline stage.

Apply it to your workflows:

Lightwheel provides an extensive library of SimReady assets optimized for robot learning, imitation learning, and video-language-action (VLA) training methods with compatibility for research benchmarks. Powered by USD Search, developers can easily search SimReady assets based on color, kinematics, and physics data.

Start by downloading assets from Lightwheel’s library or the NVIDIA open-source physical AI dataset, and take a look at them inside NVIDIA Isaac Sim.

Getting began

OpenUSD represents a paradigm shift in robotics development, moving from fragmented, tool-specific workflows to a unified, scalable, and interoperable ecosystem. By mastering data ingestion, using massive aggregated datasets, and embracing SimReady standards, robotics teams can speed up their development cycles while constructing more robust, transferable AI systems ready for the actual world.

NVIDIA provides a comprehensive collection of OpenUSD resources to speed up your learning journey. Start with the self-paced Learn OpenUSD, Digital Twins, and Robotics training curricula that construct the foundational skills covered on this guide.

For professionals able to take the subsequent steps of their robotics profession, the OpenUSD Development certification offers a professional-level exam that validates your expertise in constructing, maintaining, and optimizing 3D content pipelines using OpenUSD. Headed to NVIDIA GTC Washington D.C.? Maximize your experience by taking the certification, in person, offered at no additional charge to conference attendees.

Tune in to imminent OpenUSD Insiders livestreams and connect with the NVIDIA Developer Community. Stay awake up to now by following NVIDIA Omniverse on Instagram, LinkedIn,  X, Threads, and YouTube. 

Learn more concerning the research being showcased at CoRL and Humanoids, happening September 27-October 2 in Seoul, Korea. Also, don’t miss the keynote by NVIDIA CEO Jensen Huang at NVIDIA GTC Washington, D.C., on how breakthroughs in physical AI are powering the era of general robotics for each industry.