Robots Could Improve Everyday Life at Home or Work (ScienceDaily!)

“Assistant professor of computer science Ashutosh Saxena is working to bring such robots into homes and offices. He 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 an Ikea bookshelf and load and unload a dishwasher — all without human intervention.
Saxena, who joined the Cornell faculty in 2009, believes robots can make people’s lives better and more productive.”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.
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. Do a 360. 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.
Saxena, who led the manipulation group in the STAIR project (Stanford Artificial Intelligence Robot) at Stanford University, has researched how to make robots perceive information in cluttered and unknown environments. His work has also enabled robots to estimate depth from a single image.
“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?” he 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.
Graduate student Yun Jiang has worked on a fast, efficient algorithm to make the robotic arm identify what she calls “grasping points,” or the parts of an object that would be best to grab onto. Her main contribution has been to simultaneously find both the location and orientation of the arm when it is picking up an object.
Writing such programs involves finding the balance between the specific features of an object — from the stem of a wine glass to the handle of a brush — and the general geometric patterns that can serve as guidelines for the robot to identify.

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