Why We Built a Neuromorphic Robot to Play Foosball
For the past 25 years or so, those of us who seek to mimic the brain’s workings in silicon have held an annual workshop in the mountain town of Telluride, Colo. During those summer weeks, you can often find the participants unwinding at the bar of the New Sheridan Hotel on the town’s main street. As far back as most can remember, there has been a foosball table in the bar’s back room. During the weeks of the workshop, you’ll usually find it surrounded by a cluster of neuromorphic engineers engaged in a friendly rivalry that has spanned many years. It was therefore almost a foregone conclusion that someone was going to build a neuromorphic-robot foosball table.
That someone was me.
It turns out there’s more to the idea than simple fun. After all, why do we play competitive games like foosball? We are drawn to them for social reasons, but we also enjoy learning the mechanics and improving our performance. Games are how we boost our hand-eye coordination, tracking and predictive abilities, and strategic thinking. Those are all skills we want robots to have.
Humans have always been fascinated by the idea of machines playing our games. As far back as the late 18th century, the Mechanical Turk hoax enthralled and amazed audiences with its (fictitious) ability to beat humans at chess. But we were all just as amazed in 1997 when IBM’s Deep Blue did it for real. Now, such triumphs are almost a regular occurrence, with DeepMind’s AI systems first defeating a human champion at the board game Go, and then going on to victory with the video game StarCraft II. (An AI will probably have conquered another of your favorite games by the time you finish reading this.)
These feats of computing are pretty good measures of a system’s abilities. But they fall short in some important ways. Robots need to operate in a real world full of noise, irregularities, and evolving environments. The rigid rules and constrained environment of Go will never provide such challenges. Real-world games, certainly foosball and possibly pinball, might be a better measure of whether our efforts to match the might of the human brain are really on track.
Why are we so interested in learning biology’s computational and sensing secrets? Frankly, it’s because they are so superior to today’s computing technology, which seems to be fast reaching its limits. Commodity sensors produce too much data for computers to understand, and those computers consume too much power trying to make sense of it. Biology outperforms all our technologies when it comes to sensing and perceiving the world, and biological organisms are orders of magnitude more power efficient, reliable, robust, and adaptable.
My colleague at Western Sydney University’s International Center for Neuromorphic Systems (ICNS), André van Schaik, gives a great example: the humble mosquito. Its brain is composed of only about 200,000 neurons, yet its flight control and obstacle avoidance are far superior to anything that we have built. Next, consider the dragonfly, which can capture a mosquito midflight. It has about five times as many neurons as the mosquito and consumes perhaps 30 mosquitoes’ worth of energy per day, about equivalent to a few grains of sugar. [READ MORE]