Researchers from Carnegie Mellon University, the University of California, Berkeley, and Facebook AI didn’t just teach a robot to walk — they taught it how to learn to walk.
The distinction is key. A major hurdle to deploying legged robots, whether with two, four or even more legs, is figuring out how the robot will respond to changing conditions. Humans can adapt as they walk over rocks, mud, sand, slippery ice and uneven surfaces. They adjust to carrying a heavy backpack or limp along with an injured ankle.
Legged robots cannot adjust so quickly. Most legged robots must be hand-coded for their environments. A crack in a sidewalk or a patch of oil can stop a robot in its tracks or cause it to come tumbling down.
Rapid Motor Adaptation (RMA) seeks to change that. The artificial intelligence was jointly developed by Deepak Pathak and Zipeng Fu at CMU’s School of Computer Science and Ashish Kumar and Jitendra Malik at Berkeley AI Research. It enables legged robots to adapt intelligently in real time to challenging, unfamiliar new terrain and circumstances.
“The focus is not walking. It is learning,” said Pathak, an assistant professor in the Robotics Institute at CMU. “By falling thousands of times or millions of times in simulation, it learns to walk from scratch and adapts to the ever-changing real world.
“Since the algorithm’s focus is learning, it is applicable to any kind of robot, not just this one.”