“The difficulty with most of our systems is it’s very hard to formally prove it by doing some math, writing some equations, and saying yeah, the system is verified to be safe,” Dugar says. “It necessarily has to be done empirically.”
His robots operate alongside human workers, and ensuring that its robotic arm doesn’t hit them is obviously top of mind. To verify that in practice will requires considering all kinds of potential scenarios.
“Humans have many kinds of appearances,” Dugar points out. “Their bodies can be in different configurations. They could be kneeling, standing. They could be tripping and falling potentially. They could be crouching. They could be running. You have to respond to all these behaviors that humans could potentially exhibit on these sites, along with the variety of variations in human appearance, you know, clothes, size, shape, height, skin color, everything else.”
It’s still early days for both Safeworld and generative AI in robotics, and the company is still figuring out the best model for its product—a platform for external users, or a services based approach?—but the team is confident they are taking on the right problem.
“We’ll probably be the first profitable company in this field,” Zaho says. “Because if anyone wants to deploy, they need to pay us to handle the situation.”
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