Technology
How Aurora works.
Aurora sits between a simulation engine, a synthetic-data pipeline, and distributed GPU compute. Four ideas do the work: randomization, controllable simulation, ground truth from world state, and scale.
Synthetic Data
Domain randomization at scale.
A single world definition can generate broad distributions of training examples. Instead of one scene, Aurora sweeps lighting, materials, placement, and weather — turning a world into millions of controlled variations a model can learn from.
The hard part is not randomizing everything — it is randomizing the right things. Aurora treats the variation space itself as something to design.
Illustrative variants of one base world.
- Lighting68%
- Camera42%
- Objects77%
- Materials35%
- Weather12%
- Density54%
Simulation
Controllable simulated worlds.
Every parameter of a world is programmable. Move a camera, change a material, add occlusion, raise crowd density — and regenerate the entire dataset deterministically.
The interactive engine on the homepage demonstrates this directly. Try the randomization engine →
Ground Truth
Every pixel already knows what it is.
Because the simulator knows the underlying world state, labels are not annotated after the fact — they are read directly from the scene. No labeling queues, no human error, no ambiguity.
Compute
GPU-accelerated distributed simulation.
A generation job is split into shards, simulated and rendered in parallel across GPU workers, and merged into one coherent dataset — the same job on ten workers or ten thousand. The scheduler is provider-agnostic by design.
Get started
Build the world your machines need.
Aurora Forge is being built with a small group of robotics and perception teams. Tell us what you are working on.