A robotic system called 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, even if the item is buried under a pile and completely out of view. 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, RFusion is able to locate a tagged item within a pile.
Using machine learning, 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. The camera, antenna, robotic arm, and AI are fully integrated, so RFusion can work in any environment without requiring a special setup.
RFusion could have applications such as sorting through piles to fulfill orders in a warehouse, identifying and installing components in an auto manufacturing plant, or helping an elderly individual perform daily tasks in the home.
RFusion begins searching for an object using its antenna, which bounces signals off the RFID tag (like sunlight being reflected off a mirror) to identify a spherical area in which the tag is located. It combines that sphere with the camera input, which narrows down the object’s location; for instance, the item can’t be located on an area of a table that is empty. Once the robot has a general idea of where the item is, it would need to swing its arm widely around the room, taking additional measurements to come up with the exact location, which is slow and inefficient.
The team used reinforcement learning to train a neural network that can optimize the robot’s trajectory to the object. In such learning, the algorithm is trained through trial and error with a reward system. In the case of RFusion, the optimization algorithm was rewarded when it limited the number of moves it had to make to localize the item and the distance it had to travel to pick it up.
Once the system identifies the exact spot, the neural network uses combined RF and visual information to predict how the robotic arm should grasp the object including the angle of the hand and the width of the gripper and whether it must remove other items first. It also scans the item’s tag one last time to make sure it picked up the right object.
In the future, the researchers hope to increase the speed of the system so it can move smoothly, rather than stopping periodically to take measurements. This would enable RFusion to be deployed in a fast-paced manufacturing or warehouse setting. Beyond its potential industrial uses, a system like this could even be incorporated into future smart homes to assist people with any number of household tasks.