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The Unexpected Humanity of Robot Soccer
By Seth Frey and Patrick House
When Google’s AlphaGo computer program triumphed over a Go expert earlier this year, a human member of the Google team had to physically move the pieces. Manuela Veloso, the head of Carnegie Mellon’s machine learning department, would have done it differently. “I’d require the machine to move the pieces like I do,” she says. “That’s the world in which I live, which is a physical world.”
It sounds simple enough. If Google can make cars that drive themselves, surely it could add robotic arms to a Go match. Even in 1997, I.B.M. could have given Deep Blue robotic arms in its match against Garry Kasparov. To Veloso, though, the challenge is not in building a robot to play on a given board in given conditions, but rather to build one able to play on any board.“Imagine all the different types of chess pieces that humans handle perfectly fine. How would we get a robot to detect these pieces and move them on any type of board, with any number of lighting conditions, and never let the piece fall except on the right square? Oh God,” she says.
And if the idiosyncrasies of static chess pieces are hard for modern robotics, imagine how hard it would be to deal with a chaotic, rolling soccer ball. Then add a whole team of other robots chasing that same ball. That’s the setup for RoboCup, the annual robotic soccer competition Veloso co-founded in 1997.
The cost of bringing robots into her, and our, world is great. A game like Go is a one-versus-one game of perfect information—both sides can see the entire board and can make their moves with perfect accuracy. Two Go games could, theoretically, be identical. But the contingencies of the physical world make each soccer game different and entirely unpredictable. “As soon as you inject the ball in there—the physics of the ball, the gravity, the friction on the carpet—it’s not reproducible. How do you go about writing a piece of code that plays a game without knowing what’s going to happen?” says Veloso.
RoboCup robots range in size all the way from small (think oversized coffee mugs) to kid-sized (think 2-year-old) to adult-sized. Robot size tends to be inversely correlated with apparent soccer ability. The coffee-mug robots zip around like springtime squirrels and make what look like intentional, soccer-worthy passes and goals. In the adult-sized league, though, the robots move cautiously and inelegantly. They stumble, often. They fall, often. Videos of adult-sized robots shooting on goal need to be sped up to even be watchable (like, say, videos of plants growing).
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