Robotics labs are hunting for motion and video data to train AI models

Robots have a reading problem. Not literacy, exactly, but a shortage of the material they need to learn from. Robotics labs are now collecting motion and video data at scale to train the AI models that power their machines. The goal is simple to state and hard to pull off: teach robots to handle real-world tasks by showing them how humans do it. The push comes as the industry runs into what amounts to a data bottleneck. Building a robot that can fold laundry or sort parts requires examples of those tasks being performed. Lots of them. More footage, better robots The clearest evidence that this approach works comes from Dyna Robotics. Its Dyna-2 model saw task success climb from 20% to 53% in August 2026. The change behind that jump was the training dataset. Dyna expanded it from 1,000 hours to over 1 million hours of egocentric footage. Egocentric video is first-person footage, the kind captured from the viewpoint of whoever is doing the work. It shows the hands, the objects, and the sequence of movements from the angle a robot would actually experience. That result helps explain why massive human video pretraining has become a central pillar of robot development. It matters most f...