[info] technologyreview: robot that knows how to grab things

[Alejandro Dubrovsky]

(
http://www.technologyreview.com/printer_friendly_article.aspx?id=19768
)

Wednesday, November 28, 2007
Your Robotic Personal Assistant
New software lets robots pick up objects they have never seen before--an
important step toward creating multifunctional domestic helpers.
By Kate Greene

Aside from the Roomba, robots haven't made much progress infiltrating
American homes. But researchers at Stanford University have developed
software that overcomes one of the biggest challenges: teaching a robot
how to pick up an object it has never encountered before. The robot's
software suggests that the best way to pick up something new is by
determining the most grabable part of the object--the stem of a
wineglass, the handle of a mug, or the edge of a book, for instance.

Engineers and science-fictions fans have long dreamed of putting
robotics in the home, says Andrew Ng, professor of computer science at
Stanford. In fact, the robotic hardware that exists today could allow a
robot to do the complex tasks that are required to pick up objects, keep
a house clean, and so on. But the missing piece, Ng explains, is
software that can allow robots to do these things by themselves. A
dexterous robot with the smarts to pick up new objects without being
specifically programmed to do so could be useful for complex domestic
tasks such as feeding the pets and loading the dishwasher.

While it's true that some robots are capable of picking up specific
objects, even on a cluttered table, they do so with the help of specific
three-dimensional models that have been preprogrammed, says Aaron
Edsinger, founder of Meka Robotics, a startup in San Francisco. "But
this assumes that we're going to be able to know ahead of time what
objects are out there," he says. This might be inessential in a
carefully constructed nursing home, for instance, but it would be
essential in a busy family's apartment or house.

Instead of using predetermined models of objects, some roboticists,
including Edsinger and Ng, are building perception systems for robots
that look for certain features on objects that are good for grasping.
The Stanford team has approached the problem by collecting a number of
previously fragmented technologies, says Ng, such as computer vision,
machine learning, speech recognition, and grasping hardware, and put
them together in a robot called STAIR (Stanford Artificial Intelligence
Robot).

STAIR's hardware consists of a mobile robotic arm with a microphone, a
speaker, sensors, and cameras that help the arm retrieve objects. The
robot's software has its foundation in machine-learning algorithms that
can be trained to perform certain functions. The researchers trained the
software using 2,500 pictures of objects, with graspable regions
identified.

But making the leap from two-dimensional pictures to a three-dimensional
world was a challenge, says Ng. Typically, a robot can create a 3-D view
of its environment--so it knows how far away the coffeepot is from its
hand--using the input from two cameras. This distance is usually
determined by collecting a large number of points on an object with the
right and left cameras, and then triangulating all the data to build a
3-D model. This process takes a lot of computing power and time,
however.

Ng's team developed an alternative that simplifies the process. Instead
of collecting data about lots of points on an object, the researchers'
algorithm identifies the midpoint of a graspable portion of an object,
such as a handle, by calculating the edges of an object and comparing
this with the edges of statistically similar objects in the database.
The software matches this point using both cameras and triangulates the
distance. "This was the key idea that made all of our grasping things
work," Ng says. "We've now done things like load items from a
dishwasher."

Robots still need to learn the finer points of automatic manipulation,
Ng adds. STAIR was designed only to grasp objects, and not to adjust its
grasp depending on the situation. For instance, it wasn't built to pour
coffee from a pot--a task that might require a different grasp position
and a different amount of pressure than simply picking up the pot and
placing it on a shelf. Additionally, the software doesn't know the
consistency of the object--whether it's squishy or solid. But
researchers are working on these problems, and ultimately, a personal
robot will have a combination of sensing technologies and different
software that will allow it to pick up and manipulate an object. (See
"Robots That Sense Before They Touch.")

It could be years before all the technologies are integrated well enough
so that robots can handle complex household chores on their own, but the
Stanford work is pushing the dream forward. "If I had to pick one thing
that's holding back this vision of personal robotics, it would be the
ability to pick things up and manipulate them," says Josh Smith, senior
research scientist at Intel Research, in Seattle. "We need more grasping
strategies, like [the Stanford researchers'], that don't require an
explicit 3-D model of the object." He adds that in addition to the robot
having improved computer vision techniques, the actual hand of the robot
will most likely have a number of sensors that can feel if an object is
moving or if the grasp isn't right. "Much richer sensing in the hand
will be an important part of the solution," Smith says.
Copyright Technology Review 2007.