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Machines Like Us

Teaching robots lateral thinking

Monday, 25 February 2013
by Larry Hardesty

Jennifer Barry's algorithm enables the PR2 to slide a laden plate to the edge of a table. Photo: Allegra Boverman

New algorithms could help household robots work around their physical shortcomings.

Many commercial robotic arms perform what roboticists call “pick and place” tasks: The arm picks up an object in one location and places it in another. Usually, the objects — say, automobile components along an assembly line — are positioned so that the arm can easily grasp them; the appendage that does the grasping may even be tailored to the objects’ shape.

General-purpose household robots, however, would have to be able to manipulate objects of any shape, left in any location. And today, commercially available robots don’t have anything like the dexterity of the human hand.

At this year’s IEEE International Conference on Robotics and Automation, students in the Learning and Intelligent Systems Group at MIT’s Computer Science and Artificial Intelligence Laboratory will present a pair of papers showing how household robots could use a little lateral thinking to compensate for their physical shortcomings.

One of the papers concentrates on picking, the other on placing. Jennifer Barry, a PhD student in the group, describes an algorithm that enables a robot to push an object across a table so that part of it hangs off the edge, where it can be grasped. Annie Holladay, an MIT senior majoring in electrical engineering and computer science, shows how a two-armed robot can use one of its graspers to steady an object set in place by the other.

Colliding approaches

Most experimental general-purpose robots use a motion-planning algorithm called the rapidly exploring random tree, which maps out a limited number of collision-free trajectories through the robot’s environment — rather like a subway map overlaid on the map of a city. A sophisticated-enough robot might have arms with seven different joints; if the robot is also mounted on a mobile base — as was the Willow Garage PR2 that the MIT researchers used — then checking for collisions could mean searching a 10-dimensional space.

Add in a three-dimensional object with three different axes of orientation, which the robot has to push across a table, and the size of the search space swells to 16 dimensions, which is too large to search efficiently. Barry’s first step was to find a concise way to represent the physical properties of the object to be pushed — how it would respond to different forces applied from different directions. Armed with that description, she could characterize a much smaller space of motions that would propel the object in useful directions. “This allows us to focus the search on interesting parts of the space rather than simply flailing around in 16 dimensions,” she says. Finally, after her modification of the motion-planning algorithm, she had to “make sure that the theoretical guarantees of the planner still hold,” she says.