Runway says Praxis-1 brings internet video pretraining to robots with results close to specialist datasets

Runway has introduced Praxis-1, a robotics model that applies techniques developed for AI video training to real-world robots. The system learns from large volumes of ordinary internet video, using those clips to model how objects move and how people complete tasks, then converts that knowledge into robot actions. In an object placement test, Runway said robots pretrained on internet video posted an average error of 16.1 centimeters. When trained instead on specially collected robot teleoperation video, the average error was 16.0 centimeters. The company said the benchmark covered 93 evaluations, with the two training approaches finishing nearly even. Runway argued that if this approach scales, the amount of usable training data for robotics could expand by orders of magnitude, since teleoperated robot data requires hardware, physical space, and human labor, while ordinary video is already abundant online. Praxis-1 has been tested on multiple robots from Noble Machines, Standard Bots, and Ultra, including robotic arms, dual-arm systems, and mobile robots. The company also showed the same policy continuing a task after moving from a studio setting to a kitchen. Praxis-1 is currently available only through an early access application, while model weights are planned for release in the coming months. Parameter count and fuller independent evaluations have not yet been disclosed.