Runway has spent the past seven years building visual-generating tools for the creative industry. Now, it sees a new opportunity for its technology: robotics.
New York-based Runway is known for its video and photo generation AI world models, or large language models that create a simulated version of the real world. Most recently, the company released Gen-4, its video-generating model, in March and Runway Aleph, its video editing model, in July.
As Runway’s world models started to improve — and get more realistic — the company began to receive inbound interest from robotics and self-driving car companies looking to use the tech, Anastasis Germanidis, Runway co-founder and CTO, told TechCrunch in an interview.
“We think that this ability to simulate the world is broadly useful beyond entertainment, even though entertainment is an ever increasing and big area for us,” Germanidis said. “It makes it much more scalable and cost effective to train [robotic] policies that interact with the real world whether that’s in robotics or in self driving.”
Germanidis said working with robotics and self-driving car companies was not something Runway initially envisioned when it launched back in 2018. It wasn’t until robotics and other companies in other industries reached out, that the company realized their models had much broader use cases than they originally thought, he said.
Robotics companies are using Runway’s tech for training simulations, Germanidis said. He added that just training robots and self-driving cars in real-world scenarios is costly for companies, takes a long time, and is hard to scale.
While Runway knows it isn’t going to replace real-world training by any means, Germanidis said companies can get a lot of value running simulations on Runway’s models because they have the ability to get incredibly specific.
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Unlike in real-world training, using these models makes it easier to test for specific variables and situations without changing anything else in the scenario, he added.
“You can take a step back and then simulate the effect of different actions,” he said. “If the car took this turn over this, or perform this action, what will be the outcome of that? Creating those rollouts from the same context, is a really difficult thing to do in the physical world, to basically keep all the other aspects of the environment the same and only test the effect of the specific action you want to take.”
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