How did Stanley, the winner of Darpa's $2 million all-robot race across the Mojave, learn to drive? "In much the same way as any 16-year-old: by following the lessons of experienced humans," says the Merc-News.
When the Stanford team first started testing Stanley, a blue sport-utility vehicle, he had a 12 percent blunder rate for ``false positives'' -- incorrectly assuming 12 percent of the objects in front of him were obstacles big enough he had to swerve around them.
So the team instructed Stanley's software to take notes while a human driver maneuvered the car over different types of terrain. By following this guidance, the false positive rate dropped to one in 50,000 objects.
This kind of debugging, conducted during 1,200 miles of off-road testing in the deserts of Southern California and Arizona, put Stanley first across the finish line in Primm, Nev., after traversing a 132-mile course with no human intervention.
In some ways, this is a model for how Darpa wants to teach machines, generally. Here's a piece I wrote last year on the agency's attempt to produce cars that learn from their mistakes -- and think for themselves.
