Normally, when a robot wants to pick something up that it’s never seen before, it either has to download a 3D model of the object, make its own 3D model and analyze it, or be trained by a human on the right way to grip. Unfortunately, none of these things are really practical to do in the fast paced world of grocery checkout lines.
Researchers at Stanford University have figured out that in order to pick something up, all you really need to know is whether a piece of it has the same basic shape as the shape of your gripper. If it does, then you can mostly likely grip it tolerably well, and experimentally the success rate is better than 90 percent. Best of all, you can extract this shape information from one simple (and quick) 3D scan, even if you’ve got a big cluttered pile of stuff. Once the robot has picked up an object, it holds it up to its cameras to scan for the barcode, adds it to your tab, and bags it for you. Watch a demo of their method implemented on a PR2:
Don’t let the fact that this video is sped up by anywhere from 5x – 25x worry you; this is just research code. There’s a lot of optimizing that could be done that could increase the speed by “several orders of magnitude,” according to the researchers. And while you probably aren’t going to see PR2s down at your local Trader Joe’s, the code that’s being developed here could conceivably find its way into some kind of grocery robot in the future, or even into a robot that picks up and puts away stuff in your house.
The Stanford team—Ellen Klingbeil, Deepak Rao, Blake Carpenter, Varun Ganapathi, Andrew Y. Ng, Oussama Khatib—describe the research in a paper, “Grasping with Application to an Autonomous Checkout Robot,” presented today at the IEEE International Conference on Robotics and Automation (ICRA), in Shanghai.