The perception-action stack of embodied intelligence
Robotics is entering its data-driven era. The bitter lesson holds: hand-crafted solutions give way to learning at scale. Accordingly, robotic capability is driven by powerful models trained on massive, high-quality data.
We are a group of AI researchers, roboticists, and engineers united by this belief. We co-design the perception/model/action stack to use human dexterity at planet scale to power the next billion robots.
Building robots? Let's talk.
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All posts →Your employees already do the work robots will need to learn. How a manufacturing, construction, maintenance, or food-service business can record that work with GI EGO1 and EGO1GS headsets, run it through the GI Labs managed data pipeline, and build a reviewed dataset before buying a single robot.
Which egocentric headsets you can buy today for robot-learning data, and how GI EGO1GS, GI Ego1, Project Aria Gen 2, Quest 3S and Pico 4 Ultra rigs, and Ego-OSCAR compare on shutter, stereo geometry, synchronization, on-device processing, output, power, and price.
The most advanced headset in our Egocentric Headset family — global-shutter stereo, stereo audio, and a 400 Hz IMU — plus EgoHand, an on-device model that tells the wearer's hands from everyone else's in real time, enforcing data quality at capture time.
Our first egocentric capture device — and the perception stack that turns its raw stereo footage into metric head trajectories, 3D hand motion, and dense depth for robot learning.
A practitioner's guide to motion capture technologies for dexterous robotic manipulation — from optical tracking and electromagnetic sensing to IMU gloves and multi-sensor fusion.