MIT scientists have developed a new robot arm – RFusion – that combines data from a camera and radio frequency (RF) antenna to locate and retrieve items.
According to the scientists, RFusion can rapidly sift through clutter and even retrieve objects buried under a pile.
The RFusion prototype developed by the MIT Media Lab team relies on RFID tags, which are cheap, battery-less tags that can be stuck to an item and reflect signals sent by an antenna.
Because RF signals can travel through most surfaces (like the mound of dirty laundry that may be obscuring the keys), RFusion is able to locate a tagged item within a pile.
According to the scientists, the robotic arm uses machine learning to automatically zero-in on the object’s exact location.
It then moves the items on top of it, grasps the object, and verifies that it has picked up the right thing.
The camera, antenna, robotic arm, and AI are all fully integrated, so RFusion can work in any environment without requiring a special set up, says senior author and associate professor Fadel Adib.
“This idea of being able to find items in a chaotic world is an open problem that we’ve been working on for a few years,” notes Adib who is also the director of the Signal Kinetics group in the MIT Media Lab.
“Having robots that are able to search for things under a pile is a growing need in industry today. Right now, you can think of this as a Roomba on steroids, but in the near term, this could have a lot of applications in manufacturing and warehouse environments.”
Adib’s co-authors include lead author and research assistant Tara Boroushaki, graduate student Isaac Perper, research associate Mergen Nachin, and associate professor Alberto Rodriguez.
In order to ascertain its prowess, the MIT team tested RFusion in several different environments.
They first buried a keychain in a box full of clutter and then hid a remote control under a pile of items on a couch.
For the record, RFusion had a 96% success rate when retrieving objects that were fully hidden under a pile.
“Sometimes, if you only rely on RF measurements, there is going to be an outlier, and if you rely only on vision, there is sometimes going to be a mistake from the camera. But if you combine them, they are going to correct each other. That is what made the system so robust,” says Boroushaki.
In the future, the scientists hope to increase the speed of the system so it can move smoothly, rather than stopping periodically to take measurements.
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This could enable RFusion to be deployed in a fast-paced manufacturing or warehouse setting, notes Boroushaki.
Image and content: MIT Media Lab