Runway said it is testing Praxis-1 on a variety of embodiments and environments to identify and close potential gaps before moving to general availability. | Source: Runway
Runway AI Inc. this week introduced Praxis-1, an open-weight world action model that turns Runway’s video pretraining into control for robots. The company said it built Praxis-1 on the same large-scale video pretraining behind its general world models.
“Most robot policies are bottlenecked by robot data, which is scarce and expensive to collect,” Kamil Sindi, Runway’s chief technology officer, told The Robot Report. “Praxis-1 learns mostly from third-person video, built on the same large-scale pretraining behind Runway’s world models, so it already understands how objects behave and how tasks unfold.
“Also, performance improves as we scale video, so its ceiling is set by how much video it can learn from, not how many robot demonstrations exist,” he added.
Founded in 2018, Runway AI specializes in generative artificial intelligence research and technologies. The company offers a range of AI models, including Aleph 2.0 for in-context video editing, Act-Two for motion capture, and Gen-4.5 for text-to-video and image-to-video generation.
For robotics, Runway also offers GWM-1, a general world model. The Brooklyn, N.Y.-based company has offices in New York, San Francisco, Seattle, London, Paris, Tel Aviv, and Tokyo.
Runway has been testing Praxis-1 with early partners and plans to release the model publicly in the coming months.
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Register now and help us celebrate 20 years of RoboBusiness!Runway bets on video data for robot training
While there is a lack of real-world robotics data to train generalist AI models, there is an abundance of video data. Every day, people film and upload more of everyday life each day than any robot lab could capture through teleoperated demonstrations, noted Runway AI.
Runway has found that simulating robot policies inside its world model predicts real-world results with 0.95 correlation. This compares favorably with more expensive 3D reconstruction–based techniques, the company claimed.
In addition, Runway has extended its work on pretraining large video models into interactive, real-time video models like Solaris and GWM Worlds 2. By teaching models how to generate accurate physics, how hands move, what a task looks like partway through, the company said it has created dynamic, complex environments for agent training in the digital and physical world.
Runway said Praxis-1 brings the same approach to robotics, providing a generalist policy model for robotics developers and researchers that works across any embodiment or environment.
“Praxis-1 has been trained on a variety of manipulation tasks, ranging from straightforward pick-and-place actions, such as lifting soda cans, to more complex tasks involving deformable objects, like packing gift bags,” Sindi said.
Praxis-1 is already testing with early partners
Runway is rolling Praxis-1 out to key partners ahead of a public launch. The company is also hoping to provide access to additional partners pre-launch. Runway plans to evaluate its model across a variety of robots.
“Early partners, including Noble Machines, Standard Bots, and Ultra, are running Praxis-1 on their own hardware,” Sindi said. “A big takeaway so far is that a single model can adapt across very different embodiments, from bimanual arms to humanoids, with light fine-tuning. Their testing is helping us identify and close gaps before general availability.”
When Runway does release Praxis-1 publicly, it plans to ship it with open weights rather than as a closed model.
“We believe U.S. leadership in physical AI is critical to regaining our manufacturing lead, and that requires open American models,” Sindi said. “Open weights give hardware developers flexibility and control they don’t have today.”
Looking ahead, the company hopes to pre-launch with more select partners to continue improving its model ahead of its full launch.
“We’ll continue testing and evaluating Praxis-1 with partners, bring on more early-access partners, and assess efficacy and safety across different embodiments and environments,” Sindi said.
Runway says a policy that already understands physical plausibility and object behavior from video pretraining has an enormous head start on one built from action data alone. | Source: Runway
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