A quick look at how Graveiens AI partners with teams to deliver human data for AI models.
The industry treats robot training data as three parts: teleoperation gives precision, simulation gives scale, and real human video gives diversity. None replaces the others — production pipelines blend all three, weighted to the task and budget. We already run the diversity layer; this is how we extend into scale, without asking you to trust a synthetic-only pipeline.
Take footage we have already collected and generate synthetic variations — different lighting, clutter, object textures and camera angles — multiplying one real recording into many training-ready scenes.
We model a client's real workspace — a kitchen, a warehouse aisle, a workshop — as a simulated environment for scaled policy training before physical deployment.
Test a policy trained partly on our egocentric data inside a matching simulated environment, closing the loop between raw data delivery and a validated result.
We build on the same open, industry-standard simulation tools used across the physical-AI field — including NVIDIA Isaac Sim / Isaac Lab, MuJoCo, and open-source physics engines — rather than a closed proprietary system, so outputs stay portable to your own pipeline.
That means the synthetic scenes, digital twins and validation environments we produce are yours to take forward — no lock-in, and no bespoke format your team has to reverse-engineer later.
See egocentric data collectionThis is a newer capability we are actively building alongside our egocentric collection business. We are currently onboarding a small number of pilot partners to validate the combined real-plus-synthetic workflow before scaling. If you're evaluating synthetic augmentation or sim-to-real validation for an upcoming program, we'd like to talk.
Tell us your task and environment — we'll scope a pilot combining your egocentric data with simulation.
Book a pilot