First, HERB has randomized planning algorithms to determine the best way to grip or move something as quickly as possible. By dropping a small block on each one and then pushing it, the robot learned to classify objects either as containers—those that moved together with the block ["co-moved"] more often when pushed—or as noncontainers.
Srinivasa tells how an early version of HERB puzzled researchers when it was grabbing coffee cups to place them in a dishwasher rack. To figure out what an object is, HERB compares its live camera image with a set of 3-D models in its database, built up from representative images that researchers showed it earlier.
Another set of experiments showed the robot could learn to tell whether or not something was a container. A rough layout map of the space is first programmed into the bot. In one set of experiments, the robot was presented with 36 different objects, including hockey pucks and Tupperware.
But first, says Alexander Stoytchevan assistant professor of electrical and computer engineering at Iowa State University in Ames, robots have to be taught to do something we know instinctively: It must be trainable.
After just one action the robot had a 72 percent success rate, but its accuracy soared with each successive action, reaching Manipulating objects in cluttered settings, like carrying a pitcher through a house without spilling anything, takes two skills. It could perform five different actions with each one—grasping, pushing, tapping, shaking and dropping—and had to identify and classify them based only on the sounds they made.
HERB also uses imitation learning to figure out how to handle objects by watching how people handle them.
For example, the robot might be given 30 seconds to "think" of a way to pick up a mug; if it finds one in 15, it then has 15 more seconds to improve its plan. The robot could then use this knowledge to judge whether unfamiliar objects could hold things; in other words, it had learned, roughly, how to discern the unique characteristics of a container.
The three-foot one-meter machine balances a Barrett WAM arm and a BarrettHand atop the base of a Segway personal transporter with two small training wheels. To find its way around dynamic environments, HERB uses two laser range finders and a camera that let it tell people apart from fixed and movable objects like walls and chairs.
During a daylong public demonstration in October, HERB moved around in a model kitchen, opened cabinets and a refrigerator, and handed objects to visitors or put them in a recycling bin—all with only a few missteps.
A useful robotic assistant is about a decade away, he estimates. Srinivasa would eventually like HERB to learn some simple social rules—such as knowing to go around a group of people rather than through them—as well as how to deal with completely unfamiliar environments, even in the dark.
The team presented the machine, topped with a 3-D camerawith objects of different shapes. The robot had learned to use a perceptual model to recognize and classify objects—and it could rely on this model to estimate how similar two objects were with only the sounds they made to guide it.
It used a strange hand position, with one of its "thumbs" pointing down. Then they realized this was a "far more efficient motion" used by professional bartenders, Srinivasa says: Similarly, the days of "personal robots"—inexpensive machines that can help out at home or the office—may be closer than we think.
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