『This Robot Can Grab a Beer, with George Konidaris-EP 272』のカバーアート

This Robot Can Grab a Beer, with George Konidaris-EP 272

This Robot Can Grab a Beer, with George Konidaris-EP 272

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Open your fridge and grab a beer without knocking anything else over. It’s usually not hard for a human to do it. But getting a robot to do the same task can take an engineer weeks or months. George Konidaris is a professor of computer science at Brown University, director of the Intelligent Robot Lab, and cofounder of Realtime Robotics. Their technology does that kind of motion planning in minutes instead of weeks, and it’s running on the manufacturing lines of major automakers right now. He was on the show three years ago, when the technology worked in the lab. A lot has changed. The company closed a 45 million dollar Series B and is cash flow positive. (Blog continues below video) ******** Listen on your favorite podcast app using pod.link. . View the podcast at the bottom of this post or on our YouTube Channel. Follow us on Social and never miss an update! Facebook: https://www.facebook.com/swarfcast Instagram: https://www.instagram.com/swarfcast/ LinkedIn: https://www.linkedin.com/company/todays-machining-world Twitter: https://twitter.com/tmwswarfblog ************* Link to Graff-Pinkert’s Acquisitions and Sales promotion! ************* Main Points Grabbing a beer is harder than it looks Real Time came out of research George and his colleagues did on robot motion planning, which is how a robot decides on its own how to move without hitting anything. “There are many ways in which you could reach into your fridge and grab a beer where you would hit the fridge or a beer or knock something over and you don’t do any of those.” You solve that without thinking. For a robot it’s a hard computational problem, and the first paper posing it was written in 1979. What George and his collaborator found was a way to solve it in milliseconds instead of seconds or minutes. The original breakthrough needed a custom processor. It runs in software now. Robots are still stupid, but less so Last time he was on, he told me robots are stupid. I asked if that’s still true. They’re getting smarter and still stupid in surprising ways. Computer vision has genuinely improved. Put a robot in one room and ask it to find the door, then put it in a different room and ask again, and that works now. It used to be brittle. Two and a half years versus five Going from a car design to selling the first hundred thousand units takes an American or European automaker about five years. Chinese automakers do it in two and a half, regularly. The fastest Western car ever was the Tesla Model 3 at roughly three, and George points out that Tesla pre-sold the hundred thousand units and built the factory from scratch. He says the gap has almost nothing to do with robots. “It’s not that they’re using more robots, it’s not that they’re using less people. It’s that the design process of taking the car design and turning it into a factory that produces that car was not efficient.” Western manufacturers work sequentially. Design the car, finish it, then design the factory, then iterate on the work cells. Chinese manufacturers start designing the work cell as soon as the car sketch takes shape and accept that a lot of the work gets thrown out. There are already plenty of robots in traditional American plants. His point is they’re used the same way they were used thirty years ago, because those plants were designed thirty years ago. And established manufacturers aren’t building new plants. Every model means retooling an existing one, which he says is harder than building from nothing. The plan never works the first time This part will sound familiar to anyone who has watched a job go from print to spindle. The work cell gets designed in an office in CAD. Real Time runs the motion planning and tells them what performance to expect. Then it ships to commissioning, where somebody local assembles it with a screwdriver. “Of course it’s not exactly the same as the one in the CAD model. That’s never going to be exactly the same.” The robot moves a little differently than the one in the simulation. You run it and it doesn’t quite work. What changes is what happens next. Instead of bouncing it back to the engineer and waiting weeks, the person standing there adjusts it and reprograms in a few minutes. Will ChatGPT just do this? He gets asked constantly whether an LLM like ChatGPT or Claude will just solve motion planning. His answer is no. That kind of AI learns by studying huge piles of examples and producing something close to what it has seen. Nobody has a pile of examples of robots moving at the absolute edge of what they can do. And close isn’t the standard anyway. “You can’t like mostly not hit stuff. You have to always never hit stuff.” On a line where downtime costs tens of thousands of dollars a minute, a robot that crashes one percent of the time is not one you can install. George’s rule of automation in a factory I asked George what happens to the people in factories as ...
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