Select your localized edition:

Close ×

More Ways to Connect

Discover one of our 28 local entrepreneurial communities »

Be the first to know as we launch in new countries and markets around the globe.

Interested in bringing MIT Technology Review to your local market?

MIT Technology ReviewMIT Technology Review - logo

 

Unsupported browser: Your browser does not meet modern web standards. See how it scores »

{ action.text }

At the Advanced Telecommunications Research Institute in Kyoto, Japan, a million-dollar humanoid robot is learning to play air hockey. Using stereo video cameras, “DB” watches as a researcher strikes the puck with his paddle. Then using its hydraulically powered arm, the robot imitates the motion. After a few false starts, DB is able to hit the puck, and its movements are surprisingly graceful. This sort of “imitation learning” is yielding smarter, more adaptive robots for physical therapy, search-and-rescue missions, and space applications.

Imitation learning combines artificial-intelligence software with cutting-edge neuroscience. To learn arm movements for air hockey-or even for hitting a tennis ball-the robot uses machine vision algorithms to determine the position and velocity of a person’s limbs and maps this information to its hydraulic joints. Comparing its own movement with the original, the robot makes adjustments in real time. This is a more efficient way to make a robot perform human tasks than brute-force programming or trial and error, says computer scientist Stefan Schaal of the University of Southern California. Schaal is teaching DB new tricks in collaboration with the Japanese research group, Carnegie Mellon University, and Sarcos, a robotics company in Salt Lake City.

Until recently, however, “there was a large component of Simon Says with these robots. They didn’t understand what they were doing,” says Carnegie Mellon robotics expert Chris Atkeson. Changing one of their familiar tasks even slightly, in other words, would befuddle the machines. To add flexibility, researchers are now teaching robots to divide movements they’ve seen before into pieces that serve intermediate goals. Robots can learn to splice these building blocks together to adapt their behaviors-reaching, balancing, and even walking-to new situations. Within a few years, says Schaal, such resourceful robots could perform hazardous rescue missions, analyze stroke patients’ movements to help them refine motor skills, and replace space-walking astronauts on repair missions.

0 comments about this story. Start the discussion »

Tagged: Biomedicine

Reprints and Permissions | Send feedback to the editor

From the Archives

Close

Introducing MIT Technology Review Insider.

Already a Magazine subscriber?

You're automatically an Insider. It's easy to activate or upgrade your account.

Activate Your Account

Become an Insider

It's the new way to subscribe. Get even more of the tech news, research, and discoveries you crave.

Sign Up

Learn More

Find out why MIT Technology Review Insider is for you and explore your options.

Show Me