Meanwhile, cameras are continually adding to the library of images with each item that rolls by. If there is no mismatch, the process doesn’t disrupt the line. Utilizing the MMID sensor platform at this stage also has the advantage of being non-intrusive: if the system detects a mismatch, the error can be addressed. “We can then just recycle the incorrect item back into the system to its correct location.” Moreover, the singulated trays appear early enough in the fulfillment process that “we avoid this case where the items have made it all the way to the end of the process and someone has to deal with the error,” says Doug Morrison, a Robotics AI applied scientist who has been deeply involved in the project for the past two years. How Amazon’s Supply Chain Optimization Technologies team has evolved over time to meet a challenge of staggering complexity. The first step was simply to take pictures of products as they moved along conveyor belts in fulfillment centers, building up a library of images. But there hadn’t been a consistent effort to take images of items as they appeared in fulfillment centers, so training data wasn’t available. The team wanted to start by teaching an algorithm to match an item with its photograph. And MMID is a cornerstone for achieving this.” It will help us get packages to customers more quickly and accurately. “Solving this problem, so robots can pick up items and process them without needing to find and scan a barcode, is fundamental. “Our north star vision is to use this in robotic manipulation” says Nontas Antonakos, an applied science manager in Amazon’s computer vision group in Berlin who led the MMID team when the concept was initially conceptualized and deployed. The customer-obsessed science produced by teams in Berlin is integrated in several Amazon products and services, including retail, Alexa, robotics, and more.
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