SimReady assets for
NVIDIA Isaac Sim
Drop in 3D models, images, or text descriptions and get physics-enabled OpenUSD assets validated for Isaac Sim. Rigyd estimates mass, friction, restitution, and generates collision meshes automatically.
The problem
Why existing workflows fall short.
Manual physics annotation is slow
Isaac Sim requires USDPhysics schemas with rigid body, collision, and mass APIs. Manually annotating each asset takes hours per object.
Limited SimReady library
NVIDIA's SimReady library has ~1,000 assets. A single warehouse simulation can require 50,000+ unique objects with accurate physics.
Format conversion headaches
Getting .glb, .fbx, or .obj files into Isaac Sim-compatible OpenUSD with proper physics schemas requires multiple tools and manual steps.
How Rigyd helps
AI-native infrastructure that automates the hard parts.
Direct OpenUSD output
Rigyd outputs validated OpenUSD with PhysicsRigidBodyAPI, PhysicsMassAPI, and PhysicsCollisionAPI, ready to drop into Isaac Sim.
AI-powered physics estimation
Mass, friction, restitution, and center of mass are estimated automatically using multi-view analysis and a calibrated materials database.
Collision mesh generation
Automatic convex decomposition (V-HACD) generates collision geometry optimized for Isaac Sim's PhysX runtime.
cost reduction vs manual asset preparation
per asset, from upload to SimReady USD
assets generated for Isaac Sim workflows
Start building simulations in Isaac Sim faster
Drop in a 3D model, image, or text description and get a physics-enabled OpenUSD asset in minutes.
Frequently asked questions
Does Rigyd output valid USDPhysics schemas for Isaac Sim?
Yes. Rigyd emits OpenUSD with PhysicsRigidBodyAPI, PhysicsMassAPI, and PhysicsCollisionAPI applied, the exact schemas Isaac Sim's PhysX runtime expects. Assets drop into Isaac Sim without additional physics authoring.
How is Rigyd different from NVIDIA's SimReady asset library?
NVIDIA's SimReady library is ~1,000 curated assets for general use. Rigyd converts your own 3D catalog, typically 10,000 to 50,000 SKUs, into SimReady OpenUSD automatically, so simulations match the environment your robots actually operate in.
What file formats can I upload for Isaac Sim conversion?
Rigyd accepts .glb, .fbx, and .obj files. No pre-processing needed, geometry cleanup, collision mesh generation via V-HACD, and physics annotation happen automatically. Output is Isaac-Sim-ready OpenUSD in ~5 minutes per asset.
Are Rigyd outputs compatible with Isaac Lab and Isaac Sim 5.0?
Yes. Rigyd outputs standard USDPhysics schemas (PhysicsRigidBodyAPI, PhysicsMassAPI, PhysicsCollisionAPI) which are version-stable across Isaac Sim releases. Assets work in Isaac Sim 4.x, Isaac Sim 5.0, and Isaac Lab without modification. Articulation root and joint schemas are also standard USDPhysics, so robot assets and articulated mechanisms transfer cleanly across the stack.
How fast do Rigyd-generated assets load into Isaac Sim?
Load time scales with collision-mesh complexity. Rigyd tunes V-HACD parameters per asset class, typically 16-32 convex hulls per graspable object, so loads stay under 100ms per asset on an RTX-class GPU. For warehouse-scale scenes with 10,000+ instanced assets, USD references mean only unique geometry loads once. Memory overhead is negligible compared to manual high-poly collision approximations.
Related reading
In-depth guides on robotics simulation, OpenUSD, and SimReady assets.
9 JUL 2026
Scaling Simulation Asset Libraries Beyond Curated Inventory
Curated SimReady libraries are a great starting point but a hard ceiling. Here is why fixed inventories limit robotics simulation at scale, and how on-demand asset generation closes the gap.
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Domain Randomization for Robotics Training: Asset Diversity at Scale
Domain randomization makes trained policies transfer to the real world, but it only works if your asset library is diverse enough. Here is how asset diversity drives randomization, and how to generate it at the scale training needs.
30 JUN 2026
Building a Scalable Embodied AI Asset Pipeline: From Raw Data to Simulation
A practical look at the stages of an embodied AI asset pipeline, from raw 3D data and reference inputs to physics-ready simulation assets, and what changes when you need to produce thousands of them instead of a handful.
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