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.
Simulations Across Every Scenario and Edge Case
Domain randomization needs two things: accurate physics baselines and massive object diversity. Rigyd provides both, converting 3D models, images, or text into physics-enabled assets calibrated for robust policy training.
Updated 2026-05-08
The problem
Why existing workflows fall short.
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
Automate the hard parts.
Scale object diversity instantly
Generate thousands of unique SimReady assets from 3D models, images, or text descriptions. 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
FAQ
Frequently asked questions
Why isn't uniform random physics enough for training?
Randomizing mass uniformly between 0.1-10kg for every object produces nonsensical training distributions. Effective DR needs realistic per-object baselines, which Rigyd provides via material-based estimation.
How many unique objects can Rigyd produce for DR training?
Thousands. Enterprise API bulk-converts 3D catalogs with unique physics per object, geometry diversity AND realistic mass and friction variation, not just random texture noise.
Are Rigyd's physics values within typical DR variance?
Yes, intentionally. Mass accuracy (15-20%) and friction accuracy (0.1 coefficient) sit inside standard DR ranges, making Rigyd assets ideal baselines that randomize to realistic extremes.
What randomization ranges do production teams use with Rigyd baselines?
Typical defaults: mass ±20%, static and dynamic friction ±0.1, restitution ±0.05, center of mass ±1-2 cm for normal-sized objects. Tighter for precision tasks like peg-in-hole (mass ±5%, friction ±0.05); wider for unstructured outdoor scenarios (mass ±30%). Rigyd's estimates sit at the center of these bands, so DR variance reaches realistic extremes rather than running off into impossible-physics territory.
Does Rigyd integrate with Isaac Lab's domain randomization framework?
Yes. Isaac Lab's EventManager and physics randomization APIs operate on USDPhysics attributes, exactly what Rigyd populates. Mass, friction, and restitution randomization configs read directly from the Rigyd-generated asset's baseline values, so randomization centers itself on calibrated physics rather than requiring per-asset hand-tuning of randomization ranges.
Build diverse training datasets at scale
Bring 3D models, images, or text and get physically accurate assets for domain randomization.