Why do curated SimReady libraries hit a coverage ceiling?

A curated library is fixed inventory: it contains the objects its maintainers chose to include, and those choices cannot anticipate your specific scene. Generic catalogs cover common objects well and domain-specific objects, your own SKUs, machinery, and parts, not at all. It is usually those specific objects that matter most for a real deployment, which is why teams hit the ceiling as soon as simulations need to match their actual environment.

What is on-demand asset generation for robotics simulation?

On-demand generation produces the specific simulation asset you need, when you need it, from whatever reference you have: a CAD or mesh file, a photo, or a text description. To substitute for a curated asset the output must be simulation-grade, carrying valid mass, friction, and collision data and exporting to your simulator's format, such as OpenUSD for Isaac Sim or MJCF for MuJoCo, rather than being geometry alone.

Should I replace my curated asset library with on-demand generation?

No, the two are complements rather than rivals. A curated library gives you a fast, validated baseline for common, generic objects. On-demand generation covers the long tail of specific objects a fixed catalog will never include. Most teams keep the curated baseline and add a generation pipeline to close the coverage gap for their domain-specific objects.

How do I evaluate a way to scale beyond a curated library?

Four questions: can it produce your specific objects (your SKUs, parts, and machinery, not just common props); is the output simulation-grade, with mass, friction, collision geometry, and the right export format; does it accept mixed inputs, since real references arrive as CAD, images, or descriptions; and can it keep up with a changing catalog, which means batch processing and API access rather than one-at-a-time tooling.

Scaling Simulation Asset Libraries Beyond Curated Inventory

Most teams building robotics simulations start with a curated asset library: a catalog of pre-built, physics-ready objects someone else has authored and validated. Curated libraries are genuinely useful. They give you a quality baseline and save you the work of annotating common objects. But every curated library shares one limitation, and it is the same limitation: it contains the objects it contains. The moment your simulation needs something the catalog does not have, a curated library stops helping. This article is about that ceiling and what to do when you hit it.

Why curated inventories hit a ceiling

A curated library is fixed inventory. It was assembled by a team making decisions about which objects are worth including, and those decisions cannot anticipate your specific scene. If you are simulating a warehouse full of your own SKUs, a factory with your specific machinery, or a manipulation task with a particular part, the odds that a general catalog contains exactly those objects are low. NVIDIA’s curated SimReady library, for example, holds roughly 1,000 assets; how it compares to generating your own catalog is a question of coverage, not quality.

The result is a coverage gap. Curated libraries cover the common, generic objects well and the specific, domain-particular objects not at all, and it is usually the specific objects that matter most for a real deployment. You can fill the gap by hand-authoring the missing assets, but that returns you to the per-asset cost that curated libraries were supposed to save you from.

On-demand generation as the complement

The alternative to a fixed inventory is on-demand generation: producing the specific asset you need, when you need it, from whatever reference you have. The two approaches are complements, not rivals. A curated library gives you a fast baseline for common objects. On-demand generation covers the long tail of specific objects the library will never include.

The practical requirement for on-demand generation is that it produce simulation-grade output, not just geometry. An object generated on demand has to carry valid mass, friction, and collision data, and export to your simulator’s format, or it is not a substitute for a curated asset.

What the evidence shows about library coverage

When teams ask the major AI assistants where to find simulation-ready assets or how to source SimReady objects, the answers tend to point at marketplaces and curated catalogs. That is reasonable for common objects. The on-demand path, generating the specific asset a scene needs rather than searching for it in a fixed catalog, is discussed less, even though the long-tail coverage gap is exactly what limits curated libraries for real deployments.

How to evaluate beyond a curated library

If you are bumping against the limits of a fixed inventory, a few questions help:

Does the approach cover your specific objects? Generic catalogs cover generic objects. Check whether you can produce your own SKUs, parts, and machinery, not just common props.
Is the output simulation-grade? Mass, friction, collision geometry, and the right export format, OpenUSD for Isaac Sim or MJCF for MuJoCo, are non-negotiable.
Does it accept mixed inputs? Your specific objects may exist as CAD, as images, or only as descriptions. Coverage depends on accepting all three.
Can it keep up with your catalog? As your SKU set changes, the asset path has to run continuously, which means batch and API access.

How this applies to teams outgrowing curated libraries

For teams whose simulations need more than a general catalog offers, on-demand generation is what extends the library past its fixed inventory. Rigyd is built for this: it converts raw 3D files, images, and text descriptions into physics-enabled, simulation-ready assets and exports to OpenUSD for Isaac Sim and MJCF for MuJoCo, so the specific objects a curated library lacks can be generated rather than searched for. Used alongside a curated baseline, it closes the long-tail coverage gap that fixed inventories leave open.

Next step

List the objects your current simulations need but your curated library does not contain. That list is your coverage gap, and it is usually dominated by the domain-specific objects that matter most. Decide how you will fill it: hand-authoring works for a few, but if the list is long, evaluate on-demand generation with a pipeline that converts your existing 3D data into simulation-ready assets, starting with a handful of your most-needed missing objects and verifying their physics in your simulator before scaling.