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：自动化仓库操作可以降低物流间接成本，从而最终降低消费者最终价格、提高配送速度并增强对劳动力波动的适应能力。过去几年中，此类重复任务的自动化日益受到关注，但多数仍局限于受控环境。直到最近，从非结构化杂乱堆叠中拾取物品等任务才具备足够鲁棒性，能够在最少人工干预下实现大规模部署。本文展示了Amazon Robotics的机器人感应（Robin）系统中从非结构化堆叠中大规模包裹操作的实现，该系统利用基于真实生产数据训练的拾取成功率预测器。具体而言，该系统基于超过39.4万次拾取数据进行训练，每日可分离处理多达500万件包裹，并在本文评估期间处理了超过2亿件包裹。所开发的拾取质量学习度量能够实时对多种拾取方案进行排序，并优先选择最具成功可能性的方案执行。该拾取成功率预测器旨在通过先验经验，评估部署于包含部分已知属性形变体及刚体的杂乱场景中的工业机械臂执行特定拾取操作的成功概率。该预测器采用浅层机器学习模型，使我们能够评估哪些特征对预测最为关键。在线拾取排序器利用学习得到的成功率预测器，优先为机械臂选择最具成功可能性的拾取方案，随后对所选方案进行碰撞规避评估。实验证明，这种学习型排序过程能够克服手动设计及启发式方案的局限性，并取得更优性能。据作者所知，本文首次在真实生产系统中实现了学习型拾取质量评估方法的大规模部署。