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.

自动化仓库操作可以降低物流成本，最终降低消费者的最终价格，提高交付速度，并增强对市场波动的适应能力。本文展示了亚马逊机器人的大规模包裹处理系统，以及其使用的启发式方法和利用实际生产数据训练的拣选成功预测器的后继方法。据作者所知，这是第一个在实际生产系统中大规模部署学习拣选质量评估方法的工作。