This paper introduces Amazon Robotic Manipulation Benchmark (ARMBench), a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. Automation of operations in modern warehouses requires a robotic manipulator to deal with a wide variety of objects, unstructured storage, and dynamically changing inventory. Such settings pose challenges in perceiving the identity, physical characteristics, and state of objects during manipulation. Existing datasets for robotic manipulation consider a limited set of objects or utilize 3D models to generate synthetic scenes with limitation in capturing the variety of object properties, clutter, and interactions. We present a large-scale dataset collected in an Amazon warehouse using a robotic manipulator performing object singulation from containers with heterogeneous contents. ARMBench contains images, videos, and metadata that corresponds to 235K+ pick-and-place activities on 190K+ unique objects. The data is captured at different stages of manipulation, i.e., pre-pick, during transfer, and after placement. Benchmark tasks are proposed by virtue of high-quality annotations and baseline performance evaluation are presented on three visual perception challenges, namely 1) object segmentation in clutter, 2) object identification, and 3) defect detection. ARMBench can be accessed at http://armbench.com

翻译：本文介绍了亚马逊机器人操作基准（ARMBench），这是一个大规模、面向对象的机器人操作基准数据集，专为仓库场景设计。现代仓库的自动化运营要求机器人操作器能够处理多种物体、非结构化存储以及动态变化的库存。这些场景在操作过程中对物体身份、物理特性和状态的感知提出了挑战。现有的机器人操作数据集要么局限于少量物体，要么利用3D模型生成合成场景，难以捕获物体属性、杂乱程度和交互的多样性。我们呈现了一个在亚马逊仓库中通过机器人操作器从装有异质物品的容器中执行物体分离任务而收集的大规模数据集。ARMBench包含了对应235K+次拾放活动及190K+个独立物体的图像、视频和元数据。数据在操作的不同阶段（即拾取前、转移中和放置后）进行采集。基于高质量的标注提出了基准任务，并在三个视觉感知挑战上展示了基线性能评估，具体包括：1)杂乱场景中的物体分割、2)物体识别和3)缺陷检测。ARMBench可通过http://armbench.com访问。