Can Safeworld convince people that gen AI robots won’t hurt them?

8:11 AM ETDeveloping

Technology Desk

In Short: Safeworld, a startup founded by Kyle Wong and Simo Rachidi, emerges from stealth with a seed round of over $12 million to develop safety standards for AI-driven robots.

Safeworld, a startup founded by veteran executive Kyle Wong and machine learning engineer Simo Rachidi, has emerged from stealth with a seed round of over $12 million, led by Shine Capital and a16z Speedrun.

The company aims to address the unpredictability of generative AI models in robotics, which poses challenges for traditional safety standards.

Safeworld specializes in evaluating robotic control systems through simulations featuring realistic human models.

According to Wong, one of the key areas of focus is ensuring robots can safely navigate blind corners in factories.

Wong explained, 'What is the speed or stopping distance needed to prevent a collision with a human carrying boxes?'

To test these scenarios, Safeworld uses digital models like Genesis or MuJoCo to simulate robot-human interactions.

The startup's platform also evaluates how robots respond to human behaviors such as tripping and falling.

Vishal Dugar, CTO of Gritt Robotics, is partnering with Safeworld to develop safety simulations for robots used in industrial-scale solar farms.

Dugar noted, 'A lot of people are underestimating how hard some of these edge cases are going to be to solve.'

Jonathan Lai, a partner at a16z Speedrun, emphasized the importance of establishing safety standards now, stating, 'The time to build an industry safety standard is now while robots are being designed and deployed.'

Wong added, 'By the time you have robots in households colliding with kids and causing safety incidents, that’s way too late.'

What this adds

Safeworld's approach aims to prevent safety incidents before robots are widely deployed, addressing the complexity of unstructured environments and varying safety standards across facilities.

What's still developing

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.
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.
“One of the most common areas is if there is a blind corner in this particular factory,” Wong said.
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.
“Tripping and falling is also a good example of something that we do a lot of testing with the simulation,” Wong said.
There are definite similarities between the platform that Safeworld is building and the tools being used internally by robot builders.
Vishal Dugar, the CTO of Gritt Robotics, is developing the AI brain for robots that currently help workers install photovoltaic panels at industrial-scale solar farms, and aspire to take on more complex construction tasks.
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.

Sources

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