3D infrastructure for robotics training and evaluation
Generate physically accurate objects and worlds from sparse inputs for simulation and embodied AI systems.
Physically accurate assets at scale
No manual annotation. Upload, process, deploy. API is available for bulk operations
Upload
Drop images / 3D models or describe what you need.
AI estimates physics
Generates collision meshes. Identifies materials, estimates mass, friction, center of mass.
Download SimReady USD and MJCF
Validated OpenUSD with USDPhysics schemas. Ready for Isaac Sim, MuJoCo, Gazebo, Unreal Engine.
# SimReady Output (OpenUSD)
def "CeramicMug" (
prepend apiSchemas = ["PhysicsRigidBodyAPI", "PhysicsMassAPI"]
)
{
float physics:mass = 0.34
point3f physics:centerOfMass = (0, 0.052, 0)
rel physics:simulationOwner = </World/PhysicsScene>
def "CollisionMesh" (
prepend apiSchemas = ["PhysicsCollisionAPI"]
)
{
uniform token physics:approximation = "convexDecomposition"
float physics:friction = 0.42
float physics:restitution = 0.15
}
}<!-- SimReady Output (MJCF) -->
<mujoco model="ceramic_mug">
<option gravity="0 0 -9.81" />
<worldbody>
<body name="CeramicMug" pos="0 0 0">
<freejoint />
<inertial pos="0 0.052 0" mass="0.34"
diaginertia="0.0009 0.0009 0.0006" />
<!-- convex-decomposition collider -->
<geom name="collision" type="mesh" mesh="mug_hull"
friction="0.42 0.005 0.0001" solref="0.02 0.15" />
</body>
</worldbody>
<asset>
<mesh name="mug_hull" file="mug_hull.obj" />
</asset>
</mujoco>Simulation is only as good as its data
The bottleneck isn't compute or sim engine, it's the data going in.
Objects aren't simulation-ready
Most 3D assets lack the physical properties, mass, friction, collision geometry, that simulators need for accurate grasping, stacking, and manipulation.
Environments don't exist at scale
Training mobile robots requires thousands of diverse, physically accurate worlds. Building them by hand takes months and doesn't cover edge cases.
Variety is the real bottleneck
Domain randomization demands endless variations of objects and scenes. Without scalable 3D data generation, the sim-to-real gap stays wide open.
3D data infrastructure for creating SimReady objects and physically accurate worlds
We generate the 3D data your simulations need, objects with real physical properties, environments with collidable geometry, and endless variations for domain randomization.
3D → SimReady
Already have 3D models? We optimize the geometry and add physics properties. No pre-processing needed. Bulk processing via API is also available for enterprise plan.
Text/Image → SimReady
Describe what you need or upload images. Our AI generates the 3D geometry with required topology, texture, collision meshes, semantic labels, and physics properties.
Worlds → SimReady
Describe the environments your robots need. We orchestrate world models to generate diverse, physically accurate 3D scenes with collidable meshes — ready for navigation, manipulation, and full-scale sim-to-real training.
Compatible with
NVIDIA Isaac Sim 
Built by a team that's already scaled 3D data.
We've generated tens of thousands of 3D assets for large enterprises. In the last 12 months alone, we delivered 10 million 3D experiences. We know how to build the data infrastructure physical AI demands.
SOC 2 Type II
Enterprise-grade security with full audit trails and data protection from day one.
SSO & RBAC
Single sign-on, role-based permissions, and team management for any organization size.
Built to Scale
Generate millions of SimReady assets. From proof-of-concept to production data pipeline.
Our mission
Robotics will be the
largest industry in history.
We're creating the AI-native 3D infrastructure to make that future arrive faster.
The future isn't man or machine, it's both, working together .
Frequently asked questions
Everything you need to know about Rigyd and SimReady assets.