Robot Babies Learn To Walk The Right Way
    By News Staff | January 20th 2011 01:23 PM | Print | E-mail | Track Comments
    For robots to get 'smart', they have to learn to adapt.   University of Vermont roboticist Josh Bongard has created both simulated and actual robots that, like tadpoles becoming frogs, change their body forms while learning how to walk.

    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 those with fixed body forms and, in their final form, the changing robots had developed a sturdier gait,  better able to withstand being knocked with a stick, than the ones that had learned to walk using upright legs from the beginning.

    Bongard's research is part of so-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.

    "But we don't know how to program robots very well," Bongard says, because robots are complex systems. In some ways, they are too much like people for people to easily understand them.

    "They have lots of moving parts. And their brains, like our brains, have lots of distributed materials: there's neurons and there's sensors and motors and they're all turning on and off in parallel," Bongard says, "and the emergent behavior from the complex system which is a robot, is some useful task like clearing up a construction site or laying pavement for a new road." Or at least that's the goal.

    So far, engineers have been largely unsuccessful at creating robots that can continually perform simple, yet adaptable, behaviors in unstructured or outdoor environments so Bongard and other robotics experts have turned to computer programs to design robots and develop their behaviors -- rather than trying to program the robots' behavior directly. 

    His new work may help.

    "Morphological Change in Machines Accelerates the Evolution of Robust Behavior".  Credit: Morphology, Evolution and Cognition Laboratory, University of Vermont, 2011.  Credit: Josh Bongard, University of Vermont

    Using computer simulations, Bongard unleashed a series of synthetic beasts that move about in a 3-dimensional space, which is nothing extraordinary on its own, but then "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."




    "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.


    "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."


    That hasn't been done in robotics for an obvious reason: "it's very hard to change a robot's body," Bongard says, "it's much easier to change the programming inside its head."

    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.



    "It's a very simple prototype," he says, "but it works; it's a proof of concept."