Pieter Abbeel and Jeremy Maitin-Shepard at EECS

“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. Revealed: The video is shown at 50 times normal speed.

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. 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 Berkeley students, Marco Cusumano-Towner and Jinna Lei, 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. (This information was released in spring 2010.)

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