Inquisitive robots can help make us better teachers

To be more effective, robots need a more robust model of the world that includes the simple variations in terminology or knowledge that we take for granted. Consider the fact that something called a "red dish" or a "burgundy bowl" may in fact be the same object, or that "clean up this room" means different things to different people.To help train robots to pick up on those nuances, Chernova turned to the model of microtask management, which uses the Internet to enable short-term business tasks—transcribing audio files or categorizing a.pany's inventory, for example—Robot Frogs Trick Females in 'Bizarre' Example of Evolution to be done by workers around the world."If it's not being used, we want to have the robot say, 'I'm free right now. I want to post a job on CrowdFlower,'" said Chernova, referring to a.pany that employs a million microtask workers in 90 nations.

"'I need someone to teach me what these objects in my world are.' Hopefully a Crowdflower worker will take the job and spend five minutes labeling things in the environment for the robot, or teaching it in some other way."Recruiting people can be a challenging and inefficient project," she added. "So we like having the robot be in charge of it.""Teaching Robots to Anticipate Human Actions."To truly jumpstart their learning, robots need to ask more.plex questions, such as, "Am I doing this correctly?"Maya Cakmak, a post-doctoral student who is spending time at Willow Garage, said it's important for robots to ask questions because people aren't all that good at training them via demonstration.Humans generally don't like repeating tasks, can't perform those tasks the exact same way every time, and are disinclined to demonstrate different methods a robot might use to.plete the same task.

Inquisitive robots can help make us better teachers. Cakmak has performed studies that have helped to prove that—especially when robot programmers are non-experts.In 2012, Cakmak led a team that had volunteers guide robots through assembly tasks to construct a toy house by.bining a square block foundation with a triangular top. With passive learning, only one person in four showed the robot enough examples that it could understand how to.plete the task on its own.But when the robot asked questions about how to assemble the house, volunteers answered them—and the robot success rate soared to 100 percent. The robots processed the feedback into new actions and into mathematical functions that they could replicate later.