Simulations across
every scenario and edge case

Domain randomization needs two things: accurate physics baselines and massive object diversity. Rigyd provides both — converting any 3D model into a physics-enabled asset calibrated for robust policy training.

The problem

Why existing workflows fall short.

Variety is the real bottleneck

Effective domain randomization needs thousands of diverse objects, not just 100 objects with random textures. Object shape, mass, and material variation all matter.

Random physics is worse than no physics

Randomizing mass uniformly between 0.1-10kg for every object produces nonsensical training data. You need realistic distributions centered on accurate estimates.

Creating varied assets manually doesn't scale

Each new object variation needs geometry, physics properties, and collision meshes. At 4 hours per object, building 1,000 variations takes 4,000 engineer hours.

How Rigyd helps

AI-native infrastructure that automates the hard parts.

Scale object diversity instantly

Upload thousands of unique 3D models and get physics-enabled versions of each. Bulk API processing makes it practical to build truly diverse training sets.

Realistic physics distributions

Each object gets material-specific physics values, creating natural variation across your dataset — not uniform random noise.

DR-calibrated accuracy

Mass estimates within 15-20% and friction within 0.1 coefficient. These ranges are intentionally within typical domain randomization variance, making them ideal starting points.

97%

cost reduction in building diverse object sets

$370K

saved per 1,000-object dataset

40%

better real-world performance with diverse, accurate training data

Build diverse training datasets at scale

Upload your object library and get physics-accurate assets for domain randomization.

Starts at $29/month. 30 sim-ready objects included.