ROBOTICS
Robotics & Embodied AI
Scale simulation-to-real training pipelines with elastic GPU capacity and reproducible, sandboxed environments.
Close the sim-to-real gap
Embodied AI needs millions of rollouts. Run massively parallel simulation environments on elastic GPU capacity, and scale policy training without rewriting your stack.
Deterministic sandboxes make every rollout reproducible, so regressions in the policy are traceable to changes in the environment — not to infrastructure noise.
From lab to fleet
Export trained policies with full lineage — environment version, reward spec, and evaluation results — ready for deployment on real hardware.
FIG. 01 / SETUP
48 h
From zero to a self-improving loop
FIG. 02 / ENVIRONMENTS
2,000+
RL environments ready to compose
FIG. 03 / OVERHEAD
0
Reward engineering required
PLATFORM
Everything this solution needs. None of the overhead.
1
Environments Hub
Compose repositories, toolchains, and test harnesses into reproducible RL environments — versioned and shareable.
2
Prime-RL Training
Train across on-demand or reserved clusters, from H200 to B300 — without writing any infrastructure glue.
3
Sandboxed Rollouts
Every rollout runs isolated, so agents can execute code, browse, and call tools safely at scale.
4
Verifiable Rewards
Score outcomes against real signals — tests passing, builds compiling, diffs applying cleanly.