Researchers
enable a robot to fold towels
More than just a
household convenience, the project is a step forward in the robotic manipulation
of non-rigid objects.
By Carol Ness, NewsCenter
| 02 April 2010
BERKELEY — Who wouldn't want a robot that
could make your bed or do the laundry? Well, a team of Berkeley researchers
has brought us one important step closer by, for the first time, enabling
an autonomous robot to reliably fold piles of previously unseen towels.
Robots that can do things like assembling
cars have been around for decades. The towel-folding robot, though, is
doing something very new, according to the leaders of the Berkeley team,
doctoral student Jeremy Maitin-Shepard and Assistant Professor Pieter Abbeel
of Berkeley's Department of Electrical Engineering and Computer Sciences.
Robots like the car-assembly ones are designed
to work in highly structured settings, which allows them to perform a wide
variety of tasks with mind-boggling precision and repeatability — but only
in carefully controlled environments, Maitin-Shepard and Abbeel explain.
Outside of such settings, their capabilities are much more limited.
Pieter Abbeel and Jeremy Maitin-Shepard
at EECS
Automation of household tasks like laundry
folding is somewhat compelling in itself. But more significantly, according
to Maitin-Shepard, the task involves one that's proved a challenge for
robots: perceiving and manipulating "deformable objects" – things that
are flexible, not rigid, so their shape isn't predictable. A towel is deformable;
a mug or a computer isn't.
A video — posted on this page — tells the
story best. It shows a robot built by the Menlo Park robotics company Willow
Garage and running an algorithm developed by the Berkeley team, faced with
a heap of towels it's never "seen" before. The towels are of different
sizes, colors and materials.
The robot picks one up and turns it slowly,
first with one arm and then with the other. It uses a pair of high-resolution
cameras to scan the towel to estimate its shape. Once it finds two adjacent
corners, it can start folding. On a flat surface, it completes the folds
— smoothing the towel after each fold, and making a neat stack.
"Existing work on robotic laundry and towel
folding has shown that starting from a known configuration, the actual
folding can be performed using standard techniques in robotic manufacturing,"
says Maitin-Shepard.
But there's been a bottleneck: getting
a towel picked up from a pile where its configuration is unknown and arbitrary,
and turning it into a known, predictable shape. That's because existing
computer-vision techniques, which were primarily developed for rigid objects,
aren't robust enough to handle possible variations in three-dimensional
shape, appearance and texture that can occur with deformable objects, the
researchers say.
Solving that problem helps a robot fold
towels. But more significantly, it addresses a key issue in the development
of robotics.
"Many important problems in robotics and
computer vision involve deformable objects," says Abbeel, "and the challenges
posed by robotic towel-folding reflect important challenges inherent in
robotic perception and manipulation for deformable objects."
The team's technical innovation is a new
computer vision-based approach for detecting the key points on the cloth
for the robot to grasp, an approach that is highly effective because it
depends only on geometric cues that can be identified reliably even in
the presence of changes in appearance and texture.
The approach has proven highly reliable.
The robot succeeded in all 50 trials that were attempted on previously
unseen towels with wide variations in appearance, material and size, according
to the team's report on its research, which is being presented in May at
the International Conference on Robotics and Automation 2010 in Anchorage.
Their paper is posted online (PDF).
The system was implemented on a prototype
version of the PR2, a mobile robotic platform that was developed by Willow
Garage, using the open-source Robot Operating System (ROS) software framework.
Two undergraduates, Marco Cusumano-Towner,
a junior in EECS, and Jinna Lei, a senior math major, assisted on the project.
Maitin-Shepard's research focuses on artificial
intelligence, computer vision and machine learning. He studied computer
science at Carnegie Mellon University, earning a bachelor's degree in 2008
before coming to Berkeley.
Abbeel received bachelor's and master's
degrees in electrical engineering from KU Leuven (Belgium). He earned his
doctorate in computer science at Stanford University in 2008 and joined
the Berkeley's EECS faculty that fall. During his doctoral work, Abbeel
and collaborators developed machine-learning algorithms that enable helicopters
to learn to fly by watching an expert pilot fly — resulting in the most
advanced autonomous helicopter aerobatics to date. Abbeel's research focuses
on robotics, machine learning and control.
BACK
HOME