If robots are going to work alongside humans, the machines are going to need to swallow their pride and learn to ask for help.
At least, that’s the thinking of computer scientists at the University of Washington, who are working on ways for robots to crowdsource their problems when learning new tasks.
If successful, this approach points the way toward future robots that are capable of asking for assistance to speed up their learning when it comes to figuring out how to carry out household tasks.
The real world is extremely complicated, filled with variables, and routinely poses complex problems to robots tasked with carrying out the simplest of household chores. The UW study was aimed at finding ways to help a robot to recreate simple models out of colored Lego blocks, such as a car, tree, turtle, or a snake.
The robot couldn’t manage this, so the team hired other participants online using the Amazon Mechanical Turk crowdsourcing site to create more models of the object in question.
“We’re trying to create a method for a robot to seek help from the whole world when it’s puzzled by something,” says Rajesh Rao, associate professor of computer science and engineering and director of the Center for Sensorimotor Neural Engineering at UW. “This is a way to go beyond just one-on-one interaction between a human and a robot by also learning from other humans around the world.”