Physics-accurate
synthetic data generation

Synthetic data is only as good as the simulation that produces it. Rigyd builds the physics-accurate 3D asset layer — the foundation every synthetic dataset needs for training policies that actually transfer to real robots.

The problem

Why existing workflows fall short.

Synthetic data is bottlenecked by asset quality

Beautiful renders produced from objects with wrong mass or missing collision meshes produce training data that breaks policies on real hardware. Garbage physics in, garbage policies out.

Scale is limited by manual annotation

Synthetic data promises unlimited scale — but only if the asset-creation layer isn't the bottleneck. Hand-annotating physics caps dataset growth long before training needs it to.

Coverage requires object diversity

Robust policies need thousands of unique objects at training time. Most synthetic pipelines reuse a small asset pool, which quietly causes overfitting and poor out-of-distribution performance.

How Rigyd helps

AI-native infrastructure that automates the hard parts.

Physics layer for your rendering stack

Rigyd outputs drop into Isaac Sim Replicator, Omniverse, or custom synthetic-data pipelines — contributing the physics layer that makes rendered images and lidar scans trainable.

Bulk object generation

Convert entire 3D catalogs into physics-enabled assets. Enterprise API supports dataset pipelines with high throughput and continuous updates as new object classes are added.

Calibrated for transfer

Mass accuracy within 15–20% and friction within 0.1 coefficient — matching the variance ranges typical domain-randomization pipelines target for sim-to-real transfer.

40%

better real-world performance with physics-accurate synthetic data

97%

cost reduction in asset preparation

1,000+

unique objects per dataset becomes practical

Generate synthetic data that actually transfers

Build your synthetic dataset on a foundation of physics-accurate 3D assets.

Starts at $29/month. 30 SimReady objects included.