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.

翻译：自动化仓库操作能够降低物流间接成本，最终推动消费者终端价格下降、提升配送速度，并增强应对市场波动的韧性。本扩展摘要展示了亚马逊机器人诱导（Robin）系统中对非结构化堆叠包裹进行的大规模分拣操作，该系统每天可完成多达600万件包裹的拾取与分离，迄今已处理超过20亿件包裹。本文描述了随时间推移开发的多种启发式方法及其后续方案——该方案利用基于真实生产数据训练得到的抓取成功率预测器。据作者所知，本工作是在实际生产系统中首次大规模部署基于学习的抓取质量评估方法。