Posted on January 26, 2011  Comments (4)

In a first-of-its-kind experiment, Bongard created both simulated and actual robots that, like tadpoles becoming frogs, change their body forms while learning how to walk. And, over generations, his simulated robots also evolved, spending less time in “infant” tadpole-like forms and more time in “adult” four-legged forms.

These evolving populations of robots were able to learn to walk more rapidly than ones with fixed body forms. And, in their final form, the changing robots had developed a more robust gait — better able to deal with, say, being knocked with a stick — than the ones that had learned to walk using upright legs from the beginning.

Bongard’s research, supported by the National Science Foundation, is part of a wider venture called evolutionary robotics. “We have an engineering goal,” he says “to produce robots as quickly and consistently as possible.” In this experimental case: upright four-legged robots that can move themselves to a light source without falling over.

Using a sophisticated computer simulation, Bongard unleashed a series of synthetic beasts that move about in a 3-dimensional space. “It looks like a modern video game,” he says. Each creature — or, rather, generations of the creatures — then run a software routine, called a genetic algorithm, that experiments with various motions until it develops a slither, shuffle, or walking gait — based on its body plan — that can get it to the light source without tipping over.

“The robots have 12 moving parts,” Bongard says. “They look like the simplified skeleton of a mammal: it’s got a jointed spine and then you have four sticks — the legs — sticking out… 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.

Still, Bongard gave it a try. After running 5000 simulations, each taking 30 hours on the parallel processors in UVM’s Vermont Advanced Computing Center — “it would have taken 50 or 100 years on a single machine,” Bongard says—he took the task into the real world.

“We built a relatively simple robot, out of a couple of Lego Mindstorm kits, to demonstrate that you actually could do it,” he says. This physical robot is four-legged, like in the simulation, but the Lego creature wears a brace on its front and back legs. “The brace gradually tilts the robot,” as the controller searches for successful movement patterns, Bongard says, “so that the legs go from horizontal to vertical, from reptile to quadruped.

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