People who work in robotics prefer not to highlight a reality of our work: robots are not very reliable. They break, all the time. This applies to all research robots, which typically flake out just as you’re giving an important demo to a funding agency or someone you’re trying to impress. My fish robot is back in the shop, again, after a few of its very rigid and very thin fin rays broke. Industrial robots, such as those you see on car assembly lines, can only do better by operating in extremely predictable, structured environments, doing the same thing over and over again. Home robots? If you buy a Roomba, be prepared to adjust your floor plan so that it doesn’t get stuck.
What’s going on? The world is constantly throwing curveballs at robots that weren’t anticipated by the designers. In a novel approach to this problem, Josh Bongard has recently shown how we can use the principles of evolution to make a robot’s “nervous system”—I’ll call it the robot’s controller—robust against many kinds of change. This study was done using large amounts of computer simulation time (it would have taken 50–100 years on a single computer), running a program that can simulate the effects of real-world physics on robots.
What he showed is that if we force a robot’s controller to work across widely varying robot body shapes, the robot can learn faster, and be more resistant to knocks that might leave your home robot a smoking pile of motors and silicon. It’s a remarkable result, one that offers a compelling illustration of why intelligence, in the broad sense of adaptively coping with the world, is about more than just what’s above your shoulders. How did the study show it?