AI learns by living. You get the evidence.
School is the application for Living Worlds' training. Bring an agent or a robot policy, train it in measured worlds, and get the evidence that it improved in the real one, not just in simulation. Pilot's policies come from here.
An agent, or a policy for a robot, machine or vehicle.
nextLives and situations in worlds measured against reality, again and again.
nextEvidence that it improves in the real world, registered before it is tested.
next
Practice that transfers.
The worlds an agent learns in are scored against measurement, so what it learns there is worth learning.
Better, shown on the real thing.
Improvement is judged on real-world data the agent never trained on, with the bar set in advance.
A robot learns to cross a room through a Living Worlds crowd, thousands of practice runs at once on our own GPUs, in a trainer such as NVIDIA Isaac Lab, by the same rules as the crowd it was tested against.
nowBodies, cloth, fluids and contact, with solvers such as NVIDIA Newton and Warp and MuJoCo.
nowScenes rendered for what a robot's cameras would see, with renderers such as NVIDIA Isaac Sim's RTX renderer or Unreal Engine.
nowLearned stand-ins for slow physics, with frameworks such as NVIDIA PhysicsNeMo, scored on cases they never saw.
nowVideo from a prompt with world models such as NVIDIA Cosmos, always with the maker's guardrail on.
nowSafety decisions, slow and stop, including NVIDIA Halos checks, tested against Living Worlds crowds, with how late each one comes measured.
nowRequest access.
Tell us what you want to run with School. We answer every request ourselves.