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 workforce fluctuations. The past few years have seen increased interest in automating such repeated tasks but mostly in controlled settings. Tasks such as picking objects from unstructured, cluttered piles have only recently become robust enough for large-scale deployment with minimal human intervention. This paper demonstrates a large-scale package manipulation from unstructured piles in Amazon Robotics' Robot Induction (Robin) fleet, which utilizes a pick success predictor trained on real production data. Specifically, the system was trained on over 394K picks. It is used for singulating up to 5~million packages per day and has manipulated over 200~million packages during this paper's evaluation period. The developed learned pick quality measure ranks various pick alternatives in real-time and prioritizes the most promising ones for execution. The pick success predictor aims to estimate from prior experience the success probability of a desired pick by the deployed industrial robotic arms in cluttered scenes containing deformable and rigid objects with partially known properties. It is a shallow machine learning model, which allows us to evaluate which features are most important for the prediction. An online pick ranker leverages the learned success predictor to prioritize the most promising picks for the robotic arm, which are then assessed for collision avoidance. This learned ranking process is demonstrated to overcome the limitations and outperform the performance of manually engineered and heuristic alternatives. To the best of the authors' knowledge, this paper presents the first large-scale deployment of learned pick quality estimation methods in a real production system.

翻译：自动化仓库操作可降低物流间接成本，最终降低消费者最终价格、提升配送速度，并增强应对劳动力波动的韧性。过去几年，重复性任务的自动化技术受到广泛关注，但多数应用场景仍受控于实验室环境。从非结构化杂乱堆叠物体中完成拾取任务，直到近期才发展至具备大规模部署能力，且仅需极少的人工干预。本文展示了亚马逊机器人研发的Robin分拣系统中，基于真实生产数据训练的拾取成功率预测器，如何实现从非结构化堆叠包裹中进行大规模分拣。该系统基于超过39.4万次拾取操作训练，每日可分拣多达500万个包裹，在本文评估期间累计处理超过2亿个包裹。所提出的学习型拾取质量度量方法可实时对不同拾取方案进行排序，并优先选择最具可行性的方案执行。该拾取成功率预测器旨在通过历史经验，评估工业机械臂在包含变形体与刚体、且属性部分未知的杂乱场景中实施预期拾取的成功概率。作为浅层机器学习模型，该方法能识别对预测结果最重要的特征。在线拾取排序器利用学习型成功率预测器，为机械臂优先选择最有前景的拾取方案，随后进行碰撞规避评估。实验证明，与人工设计的启发式算法相比，该学习型排序方法能突破原有局限并提升性能。据作者所知，本文首次展示了学习型拾取质量评估方法在真实生产系统中的大规模部署应用。