Use case · Robots & embodied AI
First policy, faster.
Humanoids, arms, quadrupeds, drones — trained in simulation at scale. A robot model and a task go in; evaluated policies come out.
The shape of the work
A shipped policy stands on years of simulated practice: parallel environments, randomized scenes, curricula, and the gates that decide when simulation has earned the right to touch hardware. The gym treats that scaffolding as the product — it assembles scenes across MuJoCo, Newton, Isaac Sim, or Genesis, trains at rollout scale, and scores every candidate before hardware sees it. When the crew gets stuck, you demonstrate; the session becomes training signal.
01
Simulation at scale
Thousands of parallel rollouts across the engines the industry already trusts.
02
Demonstrations in the loop
Teach sessions capture human demonstrations as the loop runs — not annotated after it.
03
Gated sim-to-real
Hardware only sees candidates that already cleared your success criteria in simulation.
04
Retrained per task, per site
Each rebuild reuses the last one's parts, so the loop gets cheaper as it goes.
Work with us
Bring an agent and a goal.
We are taking a small number of design partners. The bar is a real workload, not a logo. Everything runs on your hardware, and you keep the machine.