Pick Planning Strategies for Large-Scale Package Manipulation
Shuai Li, Azarakhsh Keipour, Kevin Jamieson, Nicolas Hudson, Sicong, Zhao, Charles Swan, Kostas Bekris

TL;DR
This paper discusses the deployment of learned pick success predictors in Amazon's large-scale package manipulation system, improving efficiency and reliability in warehouse automation.
Contribution
It introduces the first large-scale deployment of learned pick quality estimation methods in a real production warehouse system.
Findings
Successful manipulation of over 2 billion packages
Implementation of a pick success predictor trained on real data
Improved efficiency in large-scale package handling
Abstract
Automating warehouse operations can reduce logistics overhead costs, ultimately driving down the final price for consumers, increasing the speed of delivery, and enhancing the resiliency to market fluctuations. This extended abstract showcases a large-scale package manipulation from unstructured piles in Amazon Robotics' Robot Induction (Robin) fleet, which is used for picking and singulating up to 6 million packages per day and so far has manipulated over 2 billion packages. It describes the various heuristic methods developed over time and their successor, which utilizes a pick success predictor trained on real production data. To the best of the authors' knowledge, this work is the first large-scale deployment of learned pick quality estimation methods in a real production system.
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Taxonomy
TopicsAdvanced Manufacturing and Logistics Optimization · Industrial Vision Systems and Defect Detection · Scheduling and Optimization Algorithms
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
