PR2 Robot Can Scan And Bag Your Groceries

Stanford University has their PR2 picking up items in a checkout line, scanning them, and putting them in a bag for you

2 min read
PR2 Robot Can Scan And Bag Your Groceries

pr2 grocery checkout robot

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.

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Robot with threads near a fallen branch

RoMan, the Army Research Laboratory's robotic manipulator, considers the best way to grasp and move a tree branch at the Adelphi Laboratory Center, in Maryland.

Evan Ackerman
LightGreen

“I should probably not be standing this close," I think to myself, as the robot slowly approaches a large tree branch on the floor in front of me. It's not the size of the branch that makes me nervous—it's that the robot is operating autonomously, and that while I know what it's supposed to do, I'm not entirely sure what it will do. If everything works the way the roboticists at the U.S. Army Research Laboratory (ARL) in Adelphi, Md., expect, the robot will identify the branch, grasp it, and drag it out of the way. These folks know what they're doing, but I've spent enough time around robots that I take a small step backwards anyway.

This article is part of our special report on AI, “The Great AI Reckoning.”

The robot, named RoMan, for Robotic Manipulator, is about the size of a large lawn mower, with a tracked base that helps it handle most kinds of terrain. At the front, it has a squat torso equipped with cameras and depth sensors, as well as a pair of arms that were harvested from a prototype disaster-response robot originally developed at NASA's Jet Propulsion Laboratory for a DARPA robotics competition. RoMan's job today is roadway clearing, a multistep task that ARL wants the robot to complete as autonomously as possible. Instead of instructing the robot to grasp specific objects in specific ways and move them to specific places, the operators tell RoMan to "go clear a path." It's then up to the robot to make all the decisions necessary to achieve that objective.

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