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Simulation infrastructure for physical AI

Bridging the gap between reality and simulation

SimReady assets and environments for robot policy development

See SimReady assets

01 / Why simulation

In the physical world, mistakes are not cheap.

A bug in software can be patched. A robot that fails on an assembly line, in a warehouse or in a hospital can break equipment and hurt people. Physical AI needs reliability before it ships.

  1. 01Train where it is safeSimulation lets a robot fail millions of times without consequences, and learn from every one.
  2. 02Test before you deployA policy should clear a measured reliability bar in a faithful copy of its workplace before it touches the real one.
  3. 03Close the sim-to-real gapSimulation only helps when what works there works in reality. That gap is the problem we exist to close.

02 / The pipeline

Real to sim to real.

Bring the real world into simulation, make it behave like the real world, and send policies back out that work. We are solving it one piece at a time, starting with the assets everything else is built from.

  1. 01Real

    Capture

    Objects and spaces from the real world: photos, scans, CAD.

  2. 02Sim

    Reconstruct

    The deployment site rebuilt in simulation.

    Coming soon
  3. 03Sim

    Make sim-ready

    Assets and scenes with physics, materials and joints.

    We are here
  4. 04Sim

    Evaluate

    Policies tested against reliability benchmarks.

    Coming soon
  5. 05Real

    Deploy

    Policies that transfer, with real-world feedback closing the loop.

03 / Available now

SimReady assets, made autonomously.

Give us an object, or describe one. Get back a simulation-grade asset with geometry, colliders, mass, inertia, friction, materials and joints, validated before it reaches you.

How it works
Chest of drawersRES-003 · OpenUSD · MJCFSim-ready
Realchest_of_drawers_photo.jpg
SimRES-003 · OpenUSD · MJCF
Dimensions
62 × 30 × 70 cm
Mass
17.4 kg, derived
Centre of mass
36 cm above base
Inertia
Full tensor, per body
Rigid bodies
4, fixed base
Joints
3 prismatic, 0–165 mm
Collision
30 convex hulls
Materials
Softwood, steel 304
Grasp regions
3, on the knobs
Tested in
MuJoCo 3.17

04 / Solutions

Two halves of one pipeline.

R2S · Real to simBring the place a robot will work into simulation.
R2SComing soonSpatial reconstructionRebuild a real site, such as a factory cell or a warehouse aisle, as a simulation you can train and test in.
S2R · Sim to realClose the gap so what works in sim works in the world.
S2RLiveSimReady assetsObjects, or plain-language descriptions of them, turned into simulation-grade assets with geometry, colliders, mass, materials and joints, autonomously.S2RComing soonScenesComplete, physically consistent environments composed from SimReady assets.S2RComing soonEvaluationsBenchmarks that a policy must pass in simulation before it is trusted in the real world.

05 / Who it's for

Built for teams putting robots to work.

If your robot has to learn or be tested in simulation before it is deployed, you need assets that behave like the real thing.

  1. Robot learning teamsTrain manipulation and navigation policies on objects with real physics, not placeholders.
  2. Simulation engineersStop hand-building colliders and tuning mass. Drop validated assets straight into Isaac Sim or MuJoCo.
  3. Research labsBuild benchmarks and datasets on assets that are consistent, documented and reproducible.
  4. Deployment settingsFactories, assembly lines, warehouses, hospitals, homes and labs.

Talk to us

Tell us what your simulation is missing.

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