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To assist humans around the house, robots will need to be able to deal with the unfamiliar. But while researchers can preprogram robots to do increasingly sophisticated tasks, they face a much bigger challenge in teaching them to adapt to unstructured environments. A robot developed at the University of Massachusetts Amherst, however, is able to learn to use objects that it has never encountered before.

The robot–called the UMass Mobile Manipulator, or UMan–pushes objects around on a table to see how they move. Once it identifies an object’s moving parts, it begins to experiment with it, manipulating it to perform tasks. “You can imagine a baby playing with a toy and pulling the different parts and seeing what moves how,” says lead author and graduate student Dov Katz, who did the work with Oliver Brock, a professor of computer science.

“One of the challenges in robotics is having [a robot] act intelligently, even when it doesn’t know the shape of the object,” says Andrew Ng, a computer scientist at Stanford University who works on robotic gripping.

“I think their work is an important step in this direction,” says Ng. “Previously, if someone wants a robot to use a pair of scissors, they will write a lot of software [defining] what scissors are and how the two blades move relative to each other. In contrast, Katz and Brock propose a completely new approach, where the robot plays with a pair of scissors by itself and figures out how the two blades are connected to each other.”

UMan uses a regular webcam to look down at a table from above. By analyzing differences between adjacent pixels, it guesses where an object’s edges might be found. Then it prods the object and, on the basis of how it moves, revises its estimate of the object’s shape (see video below). It continues shoving the object around, observing how its parts move in relation to each other. UMan will push the object backward and forward along its width and length and at a 45-degree angle to both, if necessary, until it’s satisfied that it understands how the object moves. Wherever the movement is restricted, the robot concludes that there’s a joint. UMan then uses that information to figure out the best way to manipulate the object. It can also tell if there are multiple joints, and how those relate to each other.

Credit: Dov Katz

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Credit: Dov Katz

Tagged: Computing, machine learning, robotic hand, grasping

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