The PR2 robot play-fights in the National Geographic courtyard in Washington, D.C.
Still image from video by Jason Kurtis, National Geographic
Mapping the Robot World
The challenge of developing artificial robot intelligence capable of learning through teaching revolves around the problem of perception. While today's robots are armed with sensors, scanners, cameras, and other high-tech tools, roboticists are still learning to help them make sense of what they encounter.
Jenkins and others who want to develop robots that can learn from demonstration—a phenomenon that roboticists call "LfD"—are in the habit of mapping the cloud of data points generated by a given demonstration action, such as picking up a glass or navigating a maze.
Then they analyze that cloud to produce learning algorithms that allow robots to perform the tasks on their own.
"What you're trying to learn when doing LfD is essentially a mathematical function," Jenkins said on a recent trip to Washington, D.C., where he was accompanied by a human-sized, 450-pound PR2 robot that looks a bit like Rosie from The Jetsons—minus the maid accoutrements.
"It's a function that takes an input—the robot's perceived state of the world—and outputs the action that the robot should take at a given time," Jenkins said.
"So the robot can autonomously say, 'Oh, here's the situation that I'm in, and here's what the human did in that situation or a situation that looked like it, so I'm going to do what the human did.'"
Getting there involves taking volumes of data and applying machine learning and statistical technologies that can produce a mathematical function map connecting what a robot sees to the action we want it to take.
"How do we give them knowledge of the 3-D world so that we can get them to do things and interact with them in the most natural way possible?" asked Jenkins, describing one of his key research questions. "Language and gestures are probably the easiest ways to make that happen." (See the Learning from Demonstration Robotics Challenge from the annual conference of the Association for the Advancement of Artificial Intelligence.)
For now, Jenkins's team and others are making steady progress toward that goal, training robots to stack blocks, pick up objects to tidy the lab space, and even boot a soccer ball.
Crowdsourcing Robot Learning
Even as they work intensely with robots in their labs, Jenkins and other roboticists say it's important to expose robots to a variety of teachers from the outside world.
Robots are better able to master learning from demonstration-type tasks when they're exposed to different demonstrations by different people. So Jenkins and others have turned to crowdsourcing to find teachers who can help train robots remotely.
The robots reside in facilities like the PR2 Remote Lab on Brown University's Providence, Rhode Island, campus, while the researchers who train them can be anywhere in the world.
The PR2 is an out-of-the-box, open-platform, R&D robot developed by Willow Garage robotics in Menlo Park, California, that can navigate in human environments and grasp or manipulate objects on command. Jenkins was on sabbatical at Willow Garage for the past year.
Chernova said that crowdsourcing is also an important step toward introducing robots into the daily lives of the masses.
