(CNBC.com) — Darwin, a robot developed by Pieter Abbeel and his team at UC Berkeley’s Robot Learning Lab, is teaching itself to learn to walk. Like a toddler, it teeters back and forth, trying and falling, and then trying again before getting it right. But it’s not actually Darwin doing all this. It’s a neural network designed to mimic the human brain.
Darwin’s baby steps speak to what many researchers believe will be the greatest leap in robotics — a kind of general machine learning that allows robots to adapt to new situations rather than respond to narrow programming. Like a child’s brain, reinforcement technology invokes the trial-and-error process. “Imagine learning a new skill, like how to ride a bike,” said John Schulman, a Ph.D. candidate in computer science at UC Berkeley in Abbeel’s group. You’re going to fall a lot, but then, “after some practice, you figure it out.”Tags: Darwin robot, John Schulman, robotics, UC Berkeley's Robot Learning Lab