Indian-American working on robots to improve daily life

Assistant professor of computer science Ashutosh Saxena at the Cornell University in the US, who did his B. Tech. from the Indian Institute of Technology, Kanpur, in 2004, is working to bring such robots into homes and offices.

Saxena, who joined the Cornell faculty in 2009, believes robots can make people's lives better and more productive, according to a Cornell release. "Just like people buy a car, I envision that in five to 10 years, people will buy an assistive robot that will be cheaper or about the same cost as a car," Saxena said.Saxena leads Cornell's Personal Robotics Lab, which develops software for complex, high-level robotics.

Among the lab's goals are programming robots that can clean up a disheveled room, assemble a bookshelf and load and unload a dishwasher -- all without human intervention.

One of the biggest technical challenges is endowing robots with the ability to learn in uncertain environments.It's one thing to make a robot do simple tasks: Pick up this pen. Move to the left. It's quite another to make a robot understand how to pick up an object it's never encountered or navigate a room it's never seen.

"For example, if you look at a new object, how would you pick it up? If you are in a new environment, how do you figure out how far away things are?" Saxena said.
On a typical afternoon in Upson Hall's Personal Robotics Lab, Saxena and his students can be found huddled around a computer perfecting the coding to make their robots come alive.

One of their research platforms is a robotic arm with a gripper. Using a camera, the robot evaluates an object -- say, a cup or plate -- and figures out how best to grab it. This technology will eventually integrate into the full-fledged dishwasher-loading robot.
Another set of students works on a roving robot with a camera. Its job is to find an object, such as a shoe, by systematically scanning the room.

"In a cluttered room, it is notoriously difficult for today's object detection algorithms to reliably find an object as simple as a shoe," Saxena said.

The key is to not look at this task in isolation, he explained. If the three-dimensional structure of the room is known, it becomes easier to find the objects.

The lab is building learning algorithms to enable roboticists to quickly combine several perception algorithms into a more reliable one.

Graduate students Congcong Li and Adarsh Kowdle presented these projects at the European Conference on Computer Vision, held in Greece Sep 5-11.

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