Our simulation framework generates customized, physically accurate robotics datasets at unprecedented scale and speed.
The fidelity gap between simulation and reality has finally closed. What was impossible 3 years ago is now the cheapest, fastest path to autonomous robotics.
Mujoco and NVIDIA Isaac Sim now simulate rigid-body physics, contact, friction, and lighting with sub-millimeter accuracy — closing the visual and dynamic gap to the real world.
NeRFs, Gaussian Splatting, and 3D foundation models let us reconstruct real-world environments into simulation assets in hours — instead of months of manual 3D modeling.
Large robotics foundation models now consume simulation data and transfer to real hardware. The Sim2Real transfer barrier — the field's blocker for a decade — is finally tractable.
Lighting conditions, friction coefficients, terrain types, obstacle configurations, and edge cases — all slider-driven, instant deployment.
Millions of physically accurate episodes on commodity GPUs. Parallel simulation environments running 24/7.
Run trained policies back through simulation to validate success rates, robustness, and failure modes before real-world deployment.
Domain-randomized output that closes the gap to real hardware. Proven transfer with π0.5 and other foundation models.
Talk to the founders about your use case — or see the platform in action with a 30-minute demo.