A faster way to teach a robot

Researchers from MIT and elsewhere have developed a technique that enables a human to efficiently fine-tune a robot that failed to complete a desired task-- like picking up a unique mug-- with very little effort on the part of the human. Imagine purchasing a robot to perform household tasks. This robot was built and trained in a factory on a certain set of tasks and has never seen the items in your home. When you ask it to pick up a mug from your kitchen table, it might not recognize your mug (perhaps because this mug is painted with an unusual image, say, of MIT's mascot, Tim the Beaver). "Right now, the way we train these robots, when they fail, we don't really know why. So you would just throw up your hands and say, 'OK, I guess we have to start over.' A critical component that is missing from this system is enabling the robot to demonstrate why it is failing so the user can give it feedback," says Andi Peng, an electrical engineering and computer science (EECS) graduate student at MIT. Peng and her collaborators at MIT, New York University, and the University of California at Berkeley created a framework that enables humans to quickly teach a robot what they want it to do, with a minimal amount of effort.