Robots learn to handle objects, understand new places

September 6, 2011
Robot Manipulation

Placing dishes in a rack is a challenging task for a robot. It must identify empty spaces and place the plate in the correct upright position. (Credit: Personal Robotics Lab)

Cornell’s Personal Robotics Laboratory computer scientists are teaching robots to manipulate objects and find their way around in new environments. They reported two examples of their work at the 2011 Robotics: Science and Systems Conference June 27 at the University of Southern California.

A common thread running through the research is “machine learning” — programming a computer to observe events and find commonalities. With the right programming, for example, a computer can look at a wide array of cups, find their common characteristics and then be able to identify cups in the future. A similar process can teach a robot to find a cup’s handle and grasp it correctly.

Other researchers have gone this far, but Saxena’s team has found that placing objects is harder than picking them up, because there are many options. A cup is placed upright on a table, but upside down in a dishwasher, so the robot must be trained to make those decisions.

“We just show the robot some examples and it learns to generalize the placing strategies and applies them to objects that were not seen before,” Saxena explained. “It learns about stability and other criteria for good placing for plates and cups, and when it sees a new object, it applies them.”

Surveying its environment with a 3-D camera, the robot randomly tests small volumes of space as suitable locations for placement. For some objects, it will test for “caging” — the presence of vertical supports that would hold an object upright. It also gives priority to “preferred” locations: A plate goes flat on a table, but upright in a dishwasher.

After training, their robot placed most objects correctly 98 percent of the time when it had seen the objects and environments previously, and 95 percent of the time when working with new objects in a new environment. Performance could be improved, the researchers suggested, by longer training.

But first, the robot has to find the dish rack. Just as we unconsciously catalog the objects in a room when we walk in, Saxena and colleague Thorsten Joachims, associate professor of computer science, have developed a system that enables a robot to scan a room and identify its objects. Pictures from the robot’s 3-D camera are stitched together to form a 3-D image of an entire room that is then divided into segments, based on discontinuities and distances between objects. The goal is to label each segment.

The researchers trained a robot by giving it 24 office scenes and 28 home scenes in which they had labeled most objects. The computer examines such features as color, texture and what is nearby and decides what characteristics all objects with the same label have in common. In a new environment, it compares each segment of its scan with the objects in its memory and chooses the ones with the best fit.