We're one step closer to full-functioning (and learning!) robots that help with everyday human tasks.
A robot can struggle to discover objects in its surroundings when it relies on computer vision alone. But by taking advantage of all of the information available to it—an object's location, size, shape and even whether it can be lifted—a robot can continually discover and refine its understanding of objects, say researchers at Carnegie Mellon University's Robotics Institute.
The Lifelong Robotic Object Discovery (LROD) process developed by the research team enabled a two-armed, mobile robot to use color video, a Kinect depth camera and non-visual information to discover more than 100 objects in a home-like laboratory, including items such as computer monitors, plants and food items.
Normally, the CMU researchers build digital models and images of objects and load them into the memory of the adorably-named HERB—the Home-Exploring Robot Butler—so the robot can recognize objects that it needs to manipulate. Virtually all roboticists do something similar to help their robots recognize objects. With the team's implementation of LROD, the robot now can discover these objects on its own.
With more time and experience, the robot gradually refines its models of the objects and begins to focus its attention on those that are most relevant to its goal—helping people accomplish tasks of daily living.
The robot's ability to discover objects on its own sometimes takes even the researchers by surprise,” said Siddhartha Srinivasa, associate professor of robotics and head of the Personal Robotics Lab, where HERB is being developed. In one case, some students left the remains of lunch—a pineapple and a bag of bagels—in the lab when they went home for the evening. The next morning, they returned to find that HERB had built digital models of both the pineapple and the bag and had figured out how it could pick up each one.
"We didn't even know that these objects existed, but HERB did," said Srinivasa, who jointly supervised the research with Martial Hebert, professor of robotics. "That was pretty fascinating."
Discovering and understanding objects in places filled with hundreds or thousands of things will be a crucial capability once robots begin working in the home and expanding their role in the workplace. Manually loading digital models of every object of possible relevance simply isn't feasible, Srinivasa said. "You can't expect Grandma to do all this," he added.
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