Physical feasibility in 3D bin packing is a key requirement in modern industrial logistics and robotic automation. With the growing adoption of industrial automation, online bin packing has gained increasing attention. However, inconsistencies in problem settings, test datasets, and evaluation metrics have hindered progress in the field, and there is a lack of a comprehensive benchmarking system. Direct testing on real hardware is costly, and building a realistic simulation environment is also challenging. To address these limitations, we introduce RoboBPP, a benchmarking system designed for robotic online bin packing. RoboBPP integrates a physics-based simulator to assess physical feasibility. In our simulation environment, we introduce a robotic arm and boxes at real-world scales to replicate real industrial packing workflows. By simulating conditions that arise in real industrial applications, we ensure that evaluated algorithms are practically deployable. In addition, prior studies often rely on synthetic datasets whose distributions differ from real-world industrial data. To address this issue, we collect three datasets from real industrial workflows, including assembly-line production, logistics packing, and furniture manufacturing. The benchmark comprises three carefully designed test settings and extends existing evaluation metrics with new metrics for structural stability and operational safety. We design a scoring system and derive a range of insights from the evaluation results. RoboBPP is fully open-source and is equipped with visualization tools and an online leaderboard, providing a reproducible and extensible foundation for future research and industrial applications (https://robot-bin-packing-benchmark.github.io).

翻译：三维装箱中的物理可行性是现代工业物流与机器人自动化的关键要求。随着工业自动化的普及，在线装箱问题日益受到关注。然而，该领域因问题设定、测试数据集和评估指标的不一致而进展受阻，且缺乏全面的基准测试体系。直接在真实硬件上测试成本高昂，构建逼真的仿真环境亦具挑战性。为应对这些局限，我们提出了RoboBPP——一个专为机器人在线装箱设计的基准测试系统。RoboBPP集成了基于物理的仿真器以评估物理可行性。在我们的仿真环境中，我们引入了真实尺度的机械臂与箱体，以复现实际工业装箱流程。通过模拟真实工业应用中出现的条件，我们确保所评估的算法具备实际部署能力。此外，先前研究常依赖于分布与现实工业数据存在差异的合成数据集。针对此问题，我们从实际工业流程中收集了三个数据集，涵盖流水线生产、物流装箱和家具制造。该基准包含三个精心设计的测试场景，并在现有评估指标基础上扩展了结构稳定性与操作安全性的新指标。我们设计了一套评分系统，并从评估结果中得出一系列洞见。RoboBPP完全开源，配备可视化工具与在线排行榜，为未来研究与工业应用提供了可复现且可扩展的基础平台（https://robot-bin-packing-benchmark.github.io）。