Cornell Robot Builds its Own "Self-Image"

Via Physorg.com -

So Cornell researchers have built a robot that works out its own model of itself and can revise the model to adapt to injury. First, it teaches itself to walk. Then, when damaged, it teaches itself to limp. Although the test robot is a simple four-legged device, the researchers say the underlying algorithm could be used to build more complex robots that can deal with uncertain situations, like space exploration, and may help in understanding human and animal behavior.

The research, reported in the latest issue (Nov. 17) of the journal Science, is by Josh Bongard, a former Cornell postdoctoral researcher now on the faculty at the University of Vermont, Cornell graduate student Viktor Zykov and Hod Lipson, Cornell assistant professor of mechanical and aerospace engineering.

Instead of giving the robot a rigid set of instructions, the researchers let it discover its own nature and work out how to control itself, a process that seems to resemble the way human and animal babies discover and manipulate their bodies. The ability to build this "self-model" is what makes it able to adapt to injury.

"Most robots have a fixed model laboriously designed by human engineers," Lipson explained. "We showed, for the first time, how the model can emerge within the robot. It makes robots adaptive at a new level, because they can be given a task without requiring a model. It opens the door to a new level of machine cognition and sheds light on the age-old question of machine consciousness, which is all about internal models."

The robot, which looks like a four-armed starfish, starts out knowing only what its parts are, not how they are arranged or how to use them to fulfill its prime directive to move forward. To find out, it applies what amounts to the scientific method: theory followed by experiment followed by refined theory.

It begins by building a series of computer models of how its parts might be arranged, at first just putting them together in random arrangements. Then it develops commands it might send to its motors to test the models. A key step, the researchers said, is that it selects the commands most likely to produce different results depending on which model is correct. It executes the commands and revises its models based on the results. It repeats this cycle 15 times, then attempts to move forward.