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). So, the robot fails.

“Right now, the way we train these robots, when they fail we don’t really know why,” says Andi Peng, an electrical engineering and computer science (EECS) graduate student at MIT. “So you would just throw up your hands and say, ‘OK, I guess we have to start over.’ A critical component that’s missing from this system is enabling the robot to demonstrate why it’s failing so the user can give it feedback.” 

Peng and her collaborators at MIT, New York University, and the University of California-Berkeley created a framework that enables humans to quickly teach a robot what they want it to do with a minimal amount of effort.

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