// TECHCRUNCH — INTELLIGENZA ARTIFICIALE
Can Safeworld convince people that GenAI robots won’t hurt them?
The big trend in robots is handing the keys over to a generative AI model, but that brings with it a problem: that architecture isn’t predictable the way traditional algorithms are. How can you be sure your brand new humanoid will be safe?
Dr. Ding Zhao, who directs the Safe AI lab at Carnegie Mellon University, has been working on this problem for almost his entire career. Now, along with veteran startup executive Kyle Wong and machine learning engineer Simo Rachidi, he’s founded a company, Safeworld, intended to solve it.
“The safety challenge that we’re talking about is a combination of, one, really advanced generative AI probabilistic evals — how do you underwrite the risk of a probabilistic system?” Zhao says. “The second part that’s really hard is the trust part, and you need both to deploy a robot.”
Safeworld is emerging from stealth today with a seed round of more than $12 million, led by Shine Capital and a16z Speedrun, with additional investment from Box Group, Carnegie Mellon University Endowment, Innovation Endeavors and SV Angel.
“The time to build an industry safety standard is now while robots are being designed and deployed,” a16z Speedrun partner Jonathan Lai told TechCurnch. “By the time you have robots in households colliding with kids and causing safety incidents, that’s way too late.”
Safeworld’s specialty is evaluating a robotic control system in simulations that are populated with realistic human models. It’s akin to the challenge faced by companies like Tesla or Wayve, which must ensure that their vehicles respond appropriately to a variety of surprising incidents they may encounter on the road. But that will be more difficult for robots, Zhao argues, because they work in unstructured environments, and because each facility they are in will have different safety standards.
“One of the most common areas is if there is a blind corner in this particular factory,” Wong said. “What is the speed or what is the stopping distance that you need to make sure that this robot will not collide with a particular human? If a human is carrying boxes, for example, will the robot detect the human or not?”
To answer that question, Safeworld will build a digital version of that corner in a model like Genesis or MuJoCo, insert a simulation of the robot it is evaluating, driven by its real software, and then run thousands of scenarios where human models encounter the robot. That’s harder than it seems, per Zhao, because people are unpredictable.
“Tripping and falling is also a good example of something that we do a lot of testing with the simulation,” Wong said. “Otherwise, you would have to go and trip and fall for the robot, which is like a hard thing to be doing all the time.”
There are definite similarities between the platform that Safeworld is building and the tools being used internally by robot builders. The founders, however, believe that beyond their specific expertise, robot-makers will want a third-party to validate their work, if only to share information about safety cases between competitors.