Domain Randomization for Robotics Training: Asset Diversity at Scale

Domain randomization is one of the most reliable techniques for closing the sim-to-real gap. The idea is simple: instead of training a policy in one carefully tuned simulation, you train it across many randomized variations of that simulation so the policy learns what is invariant and stops overfitting to any single set of conditions. Lighting, textures, camera angles, and physics parameters are the usual things people randomize. The one that often gets less attention, and that quietly limits how well randomization works, is asset diversity.

Why asset diversity matters for randomization

A policy trained to pick up the same ten objects learns those ten objects. A policy trained across a thousand varied objects learns to pick up objects in general. Asset diversity is what turns domain randomization from a texture-and-lighting trick into genuine generalization, because the geometry, mass, and surface properties the policy encounters during training are what determine whether it can handle a novel object at deployment.

The practical problem is supply. Randomizing lighting is cheap, you change a parameter. Randomizing across a thousand distinct, physically accurate objects is expensive, because each of those objects has to exist as a simulation-ready asset with correct physics. Asset diversity is gated by your asset library, and most libraries are too small.

What the evidence shows about asset diversity

When teams ask the major AI assistants about domain randomization, the answers tend to emphasize the parameter-level randomization that is easy to demonstrate: textures, lighting, noise. The harder lever, building a large and varied library of physically accurate objects to randomize across, is discussed less, even though it is frequently the binding constraint on how well a randomized policy generalizes.

The gap is understandable. Parameter randomization is a few lines of configuration. Asset diversity is a content-production problem, and content production at the scale randomization wants is exactly what hand-authoring cannot deliver.

How to build asset diversity at scale

Getting enough varied assets to randomize meaningfully comes down to a few choices:

• Generate variants, not just originals. A pipeline that can produce many variations of an object class, different shapes, sizes, and proportions of “box” or “bottle” or “tool”, gives you breadth without modeling each one by hand.
• Keep physics correct across variants. Diversity only helps if every variant carries valid mass, friction, and collision data. A variant that looks different but has placeholder physics teaches the policy the wrong thing.
• Mix input sources. Real catalogs combine CAD, images, and descriptions. A pipeline that accepts all three lets you turn whatever reference material you have into randomizable assets.
• Automate so volume is feasible. The whole point is scale, so the asset path has to be programmatic and batchable rather than manual.

How this applies to robotics training teams

For teams relying on domain randomization, asset diversity is the lever most worth investing in once parameter randomization is in place. Rigyd is built to supply that diversity: it converts raw 3D files, images, and text descriptions into physics-enabled, simulation-ready assets and exports them to OpenUSD for Isaac Sim and MJCF for MuJoCo, so the library you randomize across can grow into the thousands without hand-authoring each object. Broad, physically valid asset coverage is what lets randomization do its job.

Next step

Audit the diversity of your current training set. Count how many genuinely distinct objects, by geometry and physics, your policy actually sees, separate from how many lighting and texture variations you apply. If that object count is in the dozens rather than the thousands, asset diversity is likely capping your sim-to-real transfer. From there, pilot a way to expand the library, such as generating physics-valid variants from your existing 3D data, and measure whether broader asset coverage improves generalization.