grip.ai captures efficient, reliable, egocentric interaction data — vision, action, and outcome, perfectly aligned — so world foundation models can finally learn how the physical world responds to action.
Internet video shows what the world looks like — not what it feels like to act in it. Passive footage lacks the action labels, first-person viewpoint, and causal structure that world foundation models need to predict, plan, and control in the real world.
Scraped video has no action stream. Models see outcomes but never learn the motor commands and intent that caused them.
Simulation is cheap but physics, contact, and deformable objects still diverge from reality where it matters most.
Teleoperation rigs produce clean data at painfully low throughput and cost structures that can't reach foundation-model scale.
We build the full stack for capturing first-person physical interaction data — synchronized vision, action, and outcome — with the reliability guarantees that training frontier world models demands.
First-person, human-and-robot-viewpoint recording that matches the embodiment world models are trained to control.
Every frame is aligned with the action that produced it — hand pose, end-effector state, forces, and intent labels.
Automated QA, calibration checks, and consistency audits keep noise out of the training corpus — every batch, every time.
A capture-to-corpus pipeline engineered for throughput, driving the cost per grounded hour toward zero.
A vertically integrated pipeline that turns physical interaction into model-ready data.
Egocentric rigs record synchronized video, depth, pose, and action streams during real-world interaction.
Actions, contacts, and outcomes are aligned and labeled — turning raw footage into causal interaction data.
Automated audits validate calibration, sync, and label quality before anything enters the corpus.
Curated, deduplicated, training-ready datasets stream directly into your world-model training stack.
We're partnering with a small number of frontier teams training world foundation models and Physical AI systems. Join the waitlist to get early access.
Or reach us directly at info@grip-ai.net