AI 中文总结
针对自主物流分拣的联合多场景理解挑战，提出HUGIN训练框架，构建SortingBench基准，在多VLMs上性能提升，验证了方法有效性与实际可行性。
AI 中文摘要
自主物流分拣系统（ALSS）是具身智能的重要工业应用，需对空间上分离的相机视角进行联合规划，我们将该场景建模为联合多场景理解（JMSU）。视觉-语言模型（VLMs）具备开放世界视觉理解与任务规划能力，是JMSU的理想候选模型，但因JMSU中跨场景监督稀缺、长视觉上下文引发注意力分散，直接将现有VLMs应用于JMSU并非易事。为应对这些挑战，我们提出训练框架HUGIN，包含两个互补组件：内生数据增强在操作约束下重组已验证的原子事实；全局上下文排序使指令表示与完整视觉上下文的对齐强于与部分视觉上下文的对齐。为支撑持续研究，我们从四个自主物流分拣系统布局构建高质量工业分拣数据集与基准SortingBench。在五个开放VLMs上，HUGIN均优于匹配基线；例如Qwen3-VL-8B在SortingBench上的准确率从63.6%提升至78.8%。额外实验验证了各组件的有效性，以及JMSU在具身任务中的溢出效益；涉及15000余个包裹的部署测试，证实了基于VLM的自主物流分拣规划的实际可行性。
英文摘要
Autonomous logistics sorting systems (ALSS) are an important industrial application of embodied AI, which requires joint planning over spatially disjoint camera views. We formulate this setting as Joint Multi-Scene Understanding (JMSU). With open-world visual understanding and task-planning capabilities, vision-language models (VLMs) are promising candidates for JMSU. However, directly applying existing VLMs to JMSU is non-trivial due to scarce cross-scene supervision and attention dispersion caused by long visual context in JMSU. To address these challenges, we propose HUGIN, a training framework with two complementary components. Endogenous Data Augmentation recombines verified atomic facts under operating constraints, while Global Context Ranking aligns the instruction representation more strongly with the complete visual context than with a partial visual context. To support ongoing research, we construct a high-quality industrial sorting dataset and benchmark named SortingBench from four layouts of autonomous logistics sorting systems. Across five open VLMs, HUGIN consistently outperforms matched baselines; for example, the accuracy on SortingBench of Qwen3-VL-8B increases from 63.6% to 78.8%. Additional experiments verify the effectiveness of each component and JMSU's spillover benefits in embodied tasks. Deployment tests involving more than 15,000 packages support the practical viability of VLM-based planning for autonomous logistics sorting.
