NVIDIA's guide details a five-step workflow for preparing SimReady assets for robotics simulation, using an ABB YuMi robot and GPT-6 Astra AI model to streamline the process.
Quick Take
Key steps include STEP-file conversion, material validation, and physics configuration, ensuring assets meet SimReady Foundation specifications for effective simulation.
Key Points
The workflow includes STEP-file conversion, appearance validation, and physics configuration.
SimReady Foundation specifications guide developers in preparing assets for simulation.
NVIDIA Omniverse libraries facilitate the conversion and validation of simulation-ready assets.
GPT-6 Astra assists in automating the workflow, reducing manual preparation efforts.
ABB YuMi robot serves as a practical example for implementing the SimReady workflow.
DeepSignal Analysis
Evidence tied to original reporting
What happened
NVIDIA outlines a five-step process for preparing SimReady assets for robotics simulation, specifically using an ABB YuMi robot and the GPT-6 Astra AI model. The workflow includes converting STEP files, validating materials, and configuring physics properties to ensure compliance with SimReady Foundation specifications.
Key evidence
Developers must configure and validate materials, collision geometry, joints, and physics properties before testing robot behavior, not just convert geometry to OpenUSD.
The SimReady Foundation provides specifications and validation guidance for preparing simulation-ready assets, ensuring that developers can validate their assets against specific requirements.
NVIDIA Omniverse libraries assist developers in converting and validating simulation-ready assets, while frontier AI models like GPT-6 Astra can automate parts of this workflow.
Why it matters
The preparation of SimReady assets is crucial for effective robotics simulation, as missing simulation properties can lead to incorrect robot behavior. Proper validation ensures that assets behave as expected in simulated environments, which is essential for developing reliable robotic systems.
📖 Reader Mode
~11 min read
Elizabeth Goodman
Preparing CAD assets for robotics simulation requires more than converting geometry to OpenUSD: developers must configure and validate materials, collision geometry, joints, and other physics properties before testing robot behavior.
NVIDIA Omniverse libraries, guided by SimReady Foundation specifications and agentic NVIDIA skills, provide a structured workflow for converting and validating simulation ready (SimReady) assets. Frontier AI models can assist developers at each stage by interpreting reference materials and calling Omniverse tools on their behalf, reducing manual preparation.
This post walks through how to prepare an ABB Robotics YuMi robot for simulation using the SimReady Foundation and NVIDIA Omniverse tools, with GPT-6 Astra as one example of a frontier AI model that can assist with the process. The workflow covers five steps: STEP-file conversion, appearance validation, physics configuration, SimReady validation, and a final pick-and-place task with both arms and grippers in NVIDIA Isaac Sim.
What Is SimReady and Why Does It Matter for Simulation?
SimReady defines requirements for preparing OpenUSD assets for specific simulation use cases. Developers select a SimReady profile for their intended use and validate the asset against its requirements. Profile-specific checks validate the asset’s structure, materials, and physics properties, then flag issues for developers to fix and recheck.
A robot model can look correct but still fail in simulation if it is missing simulation properties. For example, missing collision geometry can allow objects to pass through its gripper and incorrect joints can prevent coordinated motion. Simulation properties like mass and friction directly affect whether a grasp remains stable.
The following technologies and tools support this workflow:
OpenUSD represents geometry, assembly structure, materials, and simulation properties in a common asset representation.
SimReady Foundation provides specifications and validation guidance for preparing simulation-ready assets.
NVIDIA Omniverse libraries provide capabilities that developers and agents can use to convert, inspect, and prepare OpenUSD content.
NVIDIA Isaac Sim provides the environment for configuring physics and testing robot behavior.
SimReady Robotics Workflow: An ABB YuMi Robot Example
This walkthrough demonstrates the five-step SimReady workflow using an ABB Yumi robot. GPT-6 Astra is used here as one example of a frontier AI model that can help write Python code to call NVIDIA Omniverse libraries at each stage, but the workflow can be applied across many models and robot systems.
The example uses both arms and grippers, configured during the workflow. Cubes and a Sharpie marker were used as demonstrations in step 5. Camera-based perception can be added in a separate workflow.
Create a folder inside the Isaac Sim directory and name it ‘reference’, we will use this reference folder to hold the relevant assets for the workflow.
It is important to launch Codex inside the Isaac Sim directory so that the agent can find the necessary skills which will be needed in the workflow.
In this workflow example, we used Isaac Sim 6.1 and Codex CLI and launched Isaac Sim in windowed mode.
Use the following example prompt in your active Codex CLI to start Isaac Sim:
Start Isaac Sim in windowed mode with the remote Python server enabled. Use a fresh terminal for this.
The remote Python server in Isaac Sim enables your agent to ‘talk’ via Python to your live Isaac Sim application. Confirm that the connection works before continuing.
At each step, adapt the sample prompt to your task in your frontier AI agent, then review simulation results and validation feedback to identify issues and refine the asset.
1. Import the robot from the STEP files
As the first step in this workflow, we will import the robot from the STEP files in the reference folder.
For this example, provide the following inputs in the reference folder within the Isaac Sim directory:
Provide guidance for CAD conversion, material and physics assignment, and SimReady validation.
Table 1. Sample inputs and their purposes for preparing the YuMi robot for simulation
Note: Isaac Sim 6.1 is packaged with simready-foundation-tier-core.
Ask the agent to to use the supplied inputs to guide conversion to OpenUSD and import into Isaac Sim.
Sample prompt:
Figure 1. YuMi robot and grippers imported into Isaac Sim after STEP-to-OpenUSD conversion using CAD- to-SimReady Skill
NVIDIA Skills used: omniverse-cad-to-simready, omniverse-cad-to-usd, and isaac-sim-remote
Check before continuing: The robot asset is imported and available for inspection. The task and supplied requirements are understood, and missing information is identified.
2. Match the visual appearance
The next step is to inspect the imported robot to confirm that expected materials and textures are present and loaded correctly. Compare the asset with the supplied ABB images and videos to check scale, orientation, and part placement.
Ask the agent to add or refine materials and textures using these references.
Sample prompt:
Use the ABB images and videos in the reference folder to match the imported robot’s appearance to the physical robot. Add or adjust materials and textures, and report any visual discrepancies or missing reference information.
Figure 2. YuMi asset prepared with Astra (left) alongside the ABB Robotics reference image (right)
Assumptions: The agent visually matches the robot’s materials by adjusting colors, metallic response, and roughness while preserving the CAD geometry and mounting transforms. The material settings were visual estimates, without measured material reconstruction or AI-generated textures.
Check before continuing: The robot has the expected scale, orientation, parts, and appearance. Any missing materials, textures, or other visual discrepancies are documented.
3. Configure physics properties
At this stage, the OpenUSD asset contains the robot’s visual geometry, but joints, rigid bodies, and collision geometry have not yet been configured. Ask the agent to configure and validate the physics properties for both arms and grippers. The datasheet we have in the reference folder will be used by the agent to make sure the joint ranges and speed limits match the simulated robot.
If manufacturer data is incomplete, you can ask the agent to identify missing parameters and check technical manuals or available URDF robot descriptions for the exact model. Where mass properties remain unavailable, estimate them from geometry using documented density or mass-distribution assumptions, following NVIDIA’s mass-property guidance. For example, when not given enough context, we observed the agent pulling a public URDF for the robot and used that to identify the correct axes for joints and the zero configuration.
Sample prompt:
Rig the physics and validate the physics properties for both YuMi arms and grippers.
Check joint definitions, axes and limits, mass and inertia, collision geometry, base mounting, and gripper motion.
Figure 3. YuMi physics configuration with collision geometry and joint axes during step 3
The initial pose may vary.
Figure 4. YuMi poses after physics configuration
Simulation assumptions in this workflow:
Joint configuration: The agent retrieved a public URDF for the robot and used it to identify joint axes and the zero configuration.
Per-link masses: Estimated from STEP geometry volumes and normalized to manufacturer-specified totals: 38 kg for the robot and 0.28 kg for each gripper.
Centers of mass and inertia: Calculated assuming solid parts of uniform density; internal motors, gearboxes, and wiring were not modeled individually.
Contact properties: Assumed static friction of 0.8, dynamic friction of 0.6, and zero restitution.
Collision geometry and appearance: Convex collision shapes, surface roughness, and material response were approximated.
Calibration: These parameters were not calibrated against physical YuMi measurements.
Check before continuing: Joints move around the expected axes and within their limits. Base mounting and gripper motion behave as configured. Estimated properties and any validation failures are documented.
4. Validate the SimReady properties
The next step is to validate the robot’s OpenUSD asset against the SimReady requirements for the intended simulation task. Ask the agent to run the selected SimReady Foundation checks, review the findings, and revise the asset as needed.
Sample prompt:
Validation results in this workflow:
SimReady Foundation: The YuMi asset’s units, asset dependencies, geometry, materials, rigid bodies, joints, drives, and articulation were checked against SimReady Foundation requirements.
Isaac Sim asset checks: Verified scale, orientation, part placement, gripper mounting, material coverage, and resolved texture dependencies via validation engine inside Isaac Sim.
Mass and inertia: All 21 rigid bodies had positive masses and physically admissible inertia tensors; runtime values matched authored values.
Runtime behavior: Tested arm and gripper motion, contact, release, and collisions along the tested trajectories.
These checks validated the simulation implementation. They did not establish agreement with real-world robot dynamics or collision safety across every possible pose.
Figure 5. YuMi asset in Isaac Sim after visual, physics, and SimReady validation steps
Check before continuing: The sequence meets the agreed acceptance criteria and the selected SimReady checks pass. Tuning changes, estimated properties, and any unresolved failures are documented.
5. Final task for robotic simulation
Run a final validation in Isaac Sim, combining the visual, physics, and SimReady checks with a pick-and-place task.
Example 1: Cube pick-and-place
Ask the agent to add color targets and a box to the test scene and define the target locations and color metadata, matching color targets are selected using scene metadata.
Sample prompt:
Use both arms and grippers to pick and place cubes. Add color targets and a box to the test scene, and define their locations and the targets’ color metadata.
Run the complete grasp, lift, hold, transfer, and release sequence. Use scene metadata to select the matching color target for each cube. Also test placement into the box.
Check scale, orientation, part placement, materials, textures, joint axes and limits, collision geometry, and gripper behavior. Run the selected SimReady Foundation validation checks.
Simulation assumptions:
We assumed dimensionless contact-friction coefficients of 0.8 static and 0.6 for dynamic. The grasp tests passed with these settings, but the coefficients were not measured or calibrated against the physical finger–object contact.
This example uses 45 mm, 40 g test cubes and the simulation assumptions described in Step 3.
Demonstrated pick-and-place results:
Completed cycles: Both arms and grippers completed four pick-and-place cycles in total during a 122.2-second physics simulation.
Task sequence: Each arm grasped its cube, lifted it, held it for two seconds, transferred it, and released it onto its matching color target. Each arm then picked up its cube again and placed it inside the box.
Contact behavior: Finger contact and friction carried the cubes, without attachment joints, kinematic holds, or direct cube-pose updates during simulation.
Acceptance criteria: All cycles passed the defined grasp, hold, transfer, release, and placement criteria.
Recording: The MP4 and GIF were generated from this simulation.
Figure 6. YuMi pick-and-place simulations with colored cubes and target markers
Example 2: Pick-and-place with an image-derived object
A separate demonstration used a Sharpie marker created from a reference image to have the agent create a 3D asset which the robot will pick up.
Simulation assumptions: Agent estimated the dimensions of the marker as 140mm and a 10 gram mass with the center of mass at the geometrical center of the Sharpie. Collision geometry was approximated with cylinder geometry for the cap, barrel and rear plug.
Demonstrated results: YuMi’s gripper lifted the marker from the tabletop and released it into a blue tray in Isaac Sim.
Figure 7. YuMi picking and placing a Sharpie marker modeled from a reference image in Isaac Sim
Successful simulation tests demonstrate behavior under the tested conditions. Results may vary depending on the frontier AI model and prompt used.
SimReady Foundation and NVIDIA Omniverse tools help developers turn CAD files and reference materials into assets they can configure, validate, and test in simulation. Use OpenUSD, SimReady Foundation, and NVIDIA Omniverse tools and skills to prepare assets for your robotics simulation tasks.
The YuMi walkthrough provides one example of the SimReady robotics workflow using GPT-6 Astra as the frontier AI model. To adapt the approach to another robot or frontier AI model, confirm access to the required tools and skills, supply the relevant reference materials, and repeat the asset and task validation. Results may vary with the model, reasoning effort, and prompts used.
Learn robotics and digital twin skills through the free physical AI courses.
Join us at NVIDIA GTC Berlin on October 20–22, 2026, to explore the latest developments in physical AI and robotics and connect with developers, researchers, and industry leaders.
Synthetic Data Generation for Financial AI Research with NVIDIA NeMo
AI Summary
NVIDIA's NeMo pipeline generates 502,536 unique financial news headlines in 82 iterations, addressing data imbalance in financial NLP. The iterative approach uses semantic deduplication and category-weighted sampling to enhance diversity and relevance in generated content.
Monitor the effectiveness of the workflow when applied to different robot models and systems. Additionally, observe how well the frontier AI models assist in the automation of the asset preparation process and whether they can handle variations in input data.
Deploy a Production-Ready NVIDIA AI-Q Blueprint on Oracle Cloud Infrastructure
AI Summary
The NVIDIA AI-Q Blueprint enables the deployment of advanced AI agents on Oracle Cloud Infrastructure, supporting long-horizon planning and collaboration. This open-source framework enhances AI capabilities by maintaining context across tasks and executing in a secure environment.
Deploy Long-Context Reasoning and Agentic Workflows with MiniMax M3 on NVIDIA Accelerated Infrastructure
AI Summary
NVIDIA's MiniMax M3 enables a unified system for long-context reasoning, streamlining enterprise AI workflows on NVIDIA accelerated infrastructure, including Blackwell. This reduces complexity and costs associated with managing separate models for text, vision, and code, enhancing iteration speed for developers.
NVIDIA's guide on creating SimReady assets using the GPT-6 Astra AI model provides a structured approach for robotics developers to enhance simulation accuracy and efficiency. This development signals a shift towards more accessible and streamlined workflows in robotics, which can lead to faster prototyping and reduced development costs for builders, PMs, and investors in the robotics space.
