Robot uses RF to see hidden objects
The researchers developed a picking robot, called RF-Grasp, that combines traditional computer vision with radio frequency (RF) sensing to find and grasps objects, even if they’re hidden from view. The technology, say the researchers, could aid fulfilment in e-commerce warehouses or help a machine pluck a screwdriver from a jumbled toolkit.
“Researchers have been giving robots human-like perception,” says MIT Associate Professor Fadel Adib, a co-author of a paper on the research. “We’re trying to give robots superhuman perception.”
As e-commerce continues to grow, say the researchers, warehouse work is still usually the domain of humans, not robots, despite sometimes-dangerous working conditions. That’s in part because robots struggle to locate and grasp objects in such a crowded environment.
Using optical vision alone, robots can’t perceive the presence of an item packed away in a box or hidden behind another object on the shelf. However, say the researchers, the profusion in the use of radio frequency identification (RFID) technology to track products in retail supply chains could be a boon for robots, giving them another mode of perception.
RF identification systems have two main components: a reader and a tag. The tag is a tiny computer chip that gets attached to – or, in the case of pets, implanted in – the item to be tracked. The reader then emits an RF signal, which gets modulated by the tag and reflected back to the reader. The reflected signal provides information about the location and identity of the tagged item.
“RF is such a different sensing modality than vision,” says Alberto Rodriguez, the Class of 1957 Associate Professor in the Department of Mechanical Engineering and co-author of the research paper. “It would be a mistake not to explore what RF can do.”
Consisting of a robotic arm attached to a grasping hand, RF Grasp uses both a camera and an RF reader to find and grab tagged objects, even when they’re fully blocked from the camera’s view. The camera sits on the robot’s wrist, while the RF reader stands independent of the robot and relays tracking information to the robot’s control algorithm.
So, the robot is constantly collecting both RF tracking data and a visual picture of its surroundings. Integrating these two data streams into the robot’s decision making, say the researchers, was one of the biggest challenges they faced.
“The robot has to decide, at each point in time, which of these streams is more important to think about,” says Tara Boroushaki, a research assistant in the Signal Kinetics Group at the MIT Media Lab and lead author of the paper. “It’s not just eye-hand coordination, it’s RF-eye-hand coordination. So, the problem gets very complicated.”
The robot initiates the seek-and-pluck process by pinging the target object’s RF tag for a sense of its whereabouts.
“It starts by using RF to focus the attention of vision,” says Adib. “Then you use vision to navigate fine maneuvers.”
The sequence, say the researchers, is akin to hearing a siren from behind, then turning to look and get a clearer picture of the siren’s source. With its two complementary senses, RF Grasp zeroes in on the target object. As it gets closer and even starts manipulating the item, vision, which provides much finer detail than RF, then dominates the robot’s decision making.
RF Grasp, say the researchers, proved its efficiency in a battery of tests. Compared to a similar robot equipped with only a camera, RF Grasp was able to pinpoint and grab its target object with about half as much total movement. Plus, RF Grasp displayed the unique ability to “declutter” its environment by removing packing materials and other obstacles in its way in order to access the target.
In addition to potentially being able to one day perform fulfilment in packed e-commerce warehouses, say the researchers, RF Grasp’s RF sensing could even instantly verify an item’s identity without the need to manipulate the item, expose its barcode, and then scan it.
“RF has the potential to improve some of those limitations in industry,” says Rodriguez, “especially in perception and localization.”
The researchers also envision potential home applications for the robot, like locating the right Allen wrench to assemble an item like an Ikea chair.
“Or you could imagine the robot finding lost items,” says Adib. “It’s like a super-Roomba that goes and retrieves my keys, wherever the heck I put them.”
For more, see “Robotic Grasping of Fully-Occluded Objects using RF Perception.”
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