In robot education, does evolution beat all?

Robots are great at what they do — if the job is dull and predictable. Throw in the unexpected, and robots can do the unpredictable.

The task of programming a robot’s brain for the real world can be gnarly, says Josh Bongard, an assistant professor in the University of Vermont College of Engineering and Mathematical Sciences. “It turns out that building a robot, and programming it to do something interesting is a very non-intuitive process, and it’s a difficult one for humans to do well.”

The real world, he says, “is quite messy.”

Robots, in the jargon, need “adaptive behavior” to accommodate changing circumstances, says Bongard. When programming a free-roaming robot, “We are not likely to factor in a lighting change or people moving in and out of the field of view.”

It’s not clear how animals or people make adaptations, Bongard says, “and so it’s difficult to program a robot to do them.”

Robots: Are they alive?

Bongard, like a number of roboticists, is turning to biology for answers. But he does not want to emulate living structures. Instead, he wants to use evolution to craft robot control.

The process is akin to the “artificial selection” that helped lay the foundation for the science of evolution. Darwin, after all, wrote about how animal breeders had changed their livestock by repeatedly breeding the best animals and eating the rest.

In January, 2011, Bongard reported that he had taught four-legged, digital robots to stand and run toward a light source, by grading their control software on its ability to meet those goals.

Adaptive behavior was necessary, he says, because the light source could appear anywhere, or even take evasive action, “so the robot can’t just move its legs blindly every time.”

The robots had five seconds to do or die, and their first movements were grotesque because the control software initially moved their body parts at random. After every attempt, the control programs were graded by their ability to walk, stay upright and approach the light.

It’s brutal. More than 100 million failed programs went to the virtual graveyard in the name of science, Bongard says. The programs that showed some promise were retained, randomly varied and re-tested.
The same process is found in nature, where successful genes that face random mutation are re-tested by tomorrow’s environment.

Like the average biological mutation, the mutated robot software usually failed. But over a year of supercomputer time — equivalent to 1,000 years on a desktop computer — the winning programs evolved the ability to walk toward the light.

Read more on the Why Files

Leave a Reply