01
Codifying the hardest tasks in simulation
Turn demanding physical tasks into high-fidelity, multi-modal simulation.
Build simulation infrastructure for physical AI
Codify the hardest physical tasks as high-fidelity simulation, then train, validate, and evaluate the models that run in the real world.
Stardust Synthetic Data
Train and test your perception models with photorealistic, multi-modal sensor data.
We’re getting AI into the physical world
02
Building with domain experts
Requirements come from real users operating in your domain.
03
Making simulation accessible to all
Open tools and synthetic data so any team can train, test, and evaluate physical AI.
We started where the stakes are highest, in production with the world’s most demanding teams.
NASA
BACKED BY 
A living library of physical work, across every domain
MARITIME
SPACE
AERIAL
OFF-ROAD
ROBOTICS
OPERATIONS
MANIFOLD
The evaluation platform for robotics researchers
Run every major simulator benchmark in hours, not days, see exactly where your policy fails, and compare against verified baselines.
$ manifold run █
Open source release coming soon
One harness for every major simulator
ISAAC LABMUJOCOMANISKILLGENESISSTARDUST
// evaluation today
- ✕A single eval rollout takes 24 hours or more
- ✕Every team reinvents the harness for every policy and every benchmark
- ✕No CI. Reproducibility is nearly impossible
01Every benchmarkLIBERO, RoboCasa, or your own scenarios
028× fastersharded across GPUs by default
03Track the SOTAcompare against verified baselines
04Find failurescluster episodes by failure mode
STARDUST
Synthetic data for perception and autonomy
Define a scenario in Python, or just describe it to your coding agent (Claude Code, Codex, any LLM). Stardust renders the rest: diverse, labeled, photorealistic data with rich time-series metadata, no 3D expertise required.
import bbi world = bbi.World()world.spawn("container_ship", quantity=12)world.spawn("buoy", quantity=30, scatter=True) world.ocean(sea_state=4)world.weather(fog=0.3, time="dusk")cam = world.camera(preset="maritime_eo") imgs = world.render(frames=4000)imgs.download(annotations=["bbox", "segmentation"])
SENSORS & LABELS
Multi-domain, photorealistic data matching your operational requirements
One scene, rendered across modalities in perfect registration, with ground-truth labels generated automatically for every frame.
RGB Photorealistic visible spectrum
IR Infrared and thermal bands
DEPTH Per-pixel depth and range
SEGMENTATION + BBOXES Pixel-perfect masks and 2D/3D boxes
NEURAL RENDERING
Realism and diversity with AI post-processing
A learned post-processing pass closes the domain gap and multiplies diversity. One scene, every condition: weather, lighting, and time of day, all photorealistic, so models transfer cleanly to the real world.
3D ASSETS
A 1,000+ object 3D asset library, and counting
Drag and drop from over a thousand production-ready, physically-accurate 3D assets, each tagged with real-world dimensions. Need something rare? Generate new assets with AI on demand, or request a custom asset from our 3D team.
Build physical AI at the speed of compute
From synthetic data to policy evaluation, Bifrost is the infrastructure for AI in the physical world. Tell us what you’re building.