for embodied AI.
Embodied AI teams are blocked by infrastructure, not ambition.
Hardware is scarce. Failure conditions are infinite. You can’t evaluate what you can’t run.
From policy to where it breaksin one pipeline.
Your policy goes in once. The World Model finds where it breaks; the rigs confirm those same conditions on real hardware.
Push your policy.
Find where it breaks.
4,812 explored → 36 worth testing
Confirm on real rigs.
9 real runs · fold shorts · AgiBot G1 rig
One rig. Infinite conditions.
Same rig, same task — resampled into the entire ODD: lighting, material, clutter, sensor noise.
One pipeline — augment the rig into its full ODD, forecast what breaks, prove what the policy learned, and catch it live on real hardware.
Explore DashboardYou don't need to build this. We already did.
Who it's for
Policy Teams
10× faster iteration cycles
VLA · Foundation policies
Embodied AI Startups
$150K–$500K saved on rig setup
Zero setup cost · On-demand rigs
Research & Benchmarking
16 architectures, identical conditions
Head-to-head · Bottleneck attribution
Find every bottleneck. Accelerate every iteration.
Stop rebuilding eval labs. Start making progress.