Pick Planning Strategies for Large-Scale Package Manipulation
This work addresses the challenge of large-scale package picking for warehouse automation, representing the first large-scale deployment of learned pick quality estimation in a real production system.
The paper tackles the problem of automating package manipulation from unstructured piles in Amazon's warehouse operations, achieving the manipulation of over 2 billion packages and handling up to 6 million packages per day using a learned pick success predictor.
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.