Sound anything like how a human infant first learns to roll, then crawl, then cruise along the coffee table and, finally, walk?
"Yes," says Bongard, "We're copying nature, we're copying evolution, we're copying neural science when we're building artificial brains into these robots." But the key point is that his robots don't only evolve their artificial brain -- the neural network controller -- but rather do so in continuous interaction with a changing body plan. A tadpole can't kick its legs, because it doesn't have any yet; it's learning some things legless and others with legs.
And this may help to explain the most surprising -- and useful -- finding in Bongard's study: the changing robots were not only faster in getting to the final goal, but afterward were more able to deal with new kinds of challenges that they hadn't before faced, like efforts to tip them over.
Bongard is not exactly sure why this is, but he thinks it's because controllers that evolved in the robots whose bodies changed over generations learned to maintain the desired behavior over a wider range of sensor-motor arrangements than controllers evolved in robots with fixed body plans. It seem that learning to walk while flat, squat, and then upright, gave the evolving robots resilience to stay upright when faced with new disruptions. Perhaps what a tadpole learns before it has legs makes it better able to use its legs once they grow.
"Realizing adaptive behavior in machines has to date focused on dynamic controllers, but static morphologies," Bongard writes in his PNAS paper "This is an inheritance from traditional artificial intelligence in which computer programs were developed that had no body with which to affect, and be affected by, the world."
"One thing that has been left out all this time is the obvious fact that in nature i
|Contact: Joshua Brown|
University of Vermont