Sim Studio
Purpose-built simulations
for world model training.
We build 3D simulation environments using game engine technology — then capture and deliver fully layered datasets tuned to what world model labs actually need to train on.
Real-world data can't cover the long tail. Simulation can.
Autonomous driving datasets oversample ordinary cruising and undersample the safety-critical, rare interactions that matter most. Real walking footage can't generate the terrain variation, perturbations, and recovery scenarios humanoid robotics teams need at training scale. Physical capture is expensive, slow, and legally complex for aerial and open-world scenarios.
Game engine simulations solve this: generating the specific scenarios labs need at a rate and cost physical capture never can — with every data layer synchronized from inside the render pipeline.
Spec or catalog
Work with our team to define environment, scenario parameters, and data schema — or license from our existing catalog of pre-built sims.
Build & instrument
Our studio team builds the environment in Unreal or Unity, with full render-pass capture and telemetry hooks across all eight data layers.
Capture at scale
We run capture sessions — human-driven, AI-driven, or procedurally generated — to hit the hours and scenario diversity your training spec requires.
Deliver structured
Datasets arrive synchronized, annotated, and formatted for direct ingestion. No reformatting, no missing passes, no re-capture loops.
Four environments. All eight layers. Ready to license or spec.
Each category targets a specific and rapidly growing demand signal in the world model and physical AI research landscape.
Driving
Autonomous vehicle world models are bottlenecked on long-tail data — the rare, safety-critical edge cases that real fleet data undersamples by design. Our driving sims generate urban intersections, highway merges, adverse weather, emergency vehicle interactions, and construction zones at any density and frequency a training spec requires.
- Multi-agent urban and highway environments
- Adverse weather: rain, fog, snow, night
- Rare edge cases on demand: emergency vehicles, debris, jaywalking
- Vehicle telemetry, camera pose, depth, and semantic segmentation synchronized
Human Locomotion
Humanoid robotics teams need locomotion training data at a scale physical collection cannot match. Simulation exposes a locomotion controller to millions of terrain configurations, perturbations, and recovery scenarios in the time it would take to capture a fraction physically. We build environments that cover the full motion vocabulary: walking, running, climbing, stumbling, recovering.
- Diverse terrain: stairs, ramps, uneven ground, obstacles
- Crowd navigation and multi-agent interaction
- Fall and recovery scenario generation
- Full-body joint, pose, depth, and RGB data synchronized
Aerial / Flight
Autonomous drone navigation, counter-drone systems, and aerial VLA models all require large volumes of flight data across varied conditions — data that is expensive and legally complex to capture physically. Our aerial sims cover FPV navigation, multi-rotor flight dynamics, swarm behavior, and obstacle avoidance across diverse environment types.
- FPV and fixed-wing flight across urban, terrain, and indoor environments
- Swarm coordination and multi-UAV interaction
- Adverse conditions: wind, rain, visibility degradation
- IMU, flight telemetry, depth, and optical flow synchronized
Open World
General-purpose world models need training data that captures how environments behave over time — object permanence, physics-grounded cause-and-effect, and multi-agent dynamics in complex scenes. Our open-world environments are built for this: dense, interactive, and physically consistent at scale.
- Multi-agent dynamic environments with realistic physics
- Object interaction, manipulation, and state change data
- Long-horizon temporal sequences for world state prediction
- Full RGB, segmentation, depth, normal, and motion vector passes
Every simulation ships with all eight data layers.
Captured from inside the render pipeline — not reconstructed, not estimated — every session delivers the full Layer Labs data stack, synchronized to the frame.
Spec a sim or license existing data.
Tell us the environment type, scenario parameters, and hours you need — or browse our existing catalog. We'll come back with a timeline and delivery format.