University of Vermont

Research at The University of Vermont

For Robust Robots, Let Them Be Babies First


Want to build a really tough robot? Forget about Terminator. Instead, watch a tadpole turn into a frog. Or at least that's not too far off from what University of Vermont roboticist Josh Bongard, Ph.D., has discovered, as he reports in the Proceedings of the National Academy of Sciences.

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. "This paper shows that body change, morphological change, actually helps us design better robots," Bongard says. "That's never been attempted before."

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. "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."

Which is why 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. Using a sophisticated computer simulation, Bongard unleashed a series of synthetic beasts that move about in a 3-dimensional space. 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. Some of the creatures begin flat to the ground, like tadpoles or, perhaps, snakes with legs; others have splayed legs, a bit like a lizard; and others ran the full set of simulations with upright legs, like mammals.

And why do the generations of robots that progress from slithering to wide legs and, finally, to upright legs, ultimately perform better, getting to the desired behavior faster? "The snake and reptilian robots are, in essence, training wheels," says Bongard, "they allow evolution to find motion patterns quicker, because those kinds of robots can't fall over." After solving the challenge of movement as one discrete problem, the robots can then tackle balance as a separate issue.

After running 5,000 simulations, each taking 30 hours on the parallel processors in UVM's Vermont Advanced Computing Core, Bongard took the task into the real world, building a relatively simple robot out of Lego Mindstorm kits. The physical robot is four-legged, like in the simulation, but the Lego creature wears a brace on its front and back legs, which tilts it as the controller searches for successful movement patterns.

"While the brace is bending the legs, the controller is causing the robot to move around, so it's able to move its legs, and bend its spine," he says, "it's squirming around like a reptile flat on the ground and then it gradually stands up until, at the end of this movement pattern, it's walking like a coyote. It's a very simple prototype, but it works; it's a proof of concept."

Last modified May 26 2015 11:02 AM