A Sniffer Dog, But Make It A Robot, And Just An Arm

A Sniffer Dog, But Make It A Robot, And Just An Arm
Image: MIT

A robot that finds lost items around your home could soon be on the horizon, thanks to an innovation out of MIT. Researchers say the robotic system they’ve created can ‘rapidly’ help you sift through clutter to find something you think you’ve lost. They’ve labelled the robot arm a ‘Roomba on steroids’.

The robot arm system fuses data from a camera and antenna to locate and retrieve items, even if they are buried under a pile.

The robotic arm automatically zeroes-in on the object’s exact location, moves the items on top of it, grasps the object and verifies that it picked up the right thing (this is all through the use of machine learning).

The system, RFusion, is a robotic arm with a camera and radio frequency (RF) antenna attached to its gripper. It fuses signals from the antenna with visual input from the camera to locate and retrieve an item.

The RFusion prototype 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 keys, for example), RFusion is able to locate a tagged item within a pile, MIT explains.

MIT has big plans for RFusion. It reckons the robot could have many broader applications in the future, like sorting through piles to fulfil orders in a warehouse, identifying and installing components in an auto manufacturing plant or helping an elderly individual perform daily tasks in the home.

“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,” MIT associate professor Fadel Adib said.

“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.”

The current RFusion prototype isn’t quite fast enough yet for that, but it no doubt will learn more very, very soon.