面向物流具身智能的数据飞轮
Towards a Data Flywheel for Embodied Intelligence in Logistics
AI总结：
提出一种数据驱动的物流具身智能框架，通过构建数据飞轮将日常操作转化为可复用数据资产，利用世界模型生成长尾包裹操作的可靠监督，并整合多模态数据实现策略持续改进。
摘要：
具身智能正从实验室演示走向工业部署，物流行业是其中的关键应用场景。基于学习的策略为超越传统感知-规划-控制流程提供了有前景的路径，但其可扩展性取决于具身数据的收集、组织和复用方式。本研究通过构建物流数据飞轮，探索面向工业具身智能的数据中心框架。我们的框架将日常操作转化为可复用的数据资产，利用世界模型为长尾包裹操作生成可靠监督，并将部署反馈反馈到策略改进中。作为初步成果， extit{WM-DAgger}引入了一种基于世界模型的数据聚合框架，该框架合成了分布外恢复数据，用于鲁棒的模仿学习。在此成果基础上，正在进行的工作探索如何将大规模野外多模态数据（包括标注的人类演示、未标注的操作视频以及系统级机器人日志）对齐用于策略学习，并将其转化为持续系统改进的反馈。
英文摘要：
Embodied intelligence is moving from laboratory demonstrations toward industrial deployment, with the logistics industry serving as a key application scenario. Learning-based policies offer a promising path beyond traditional perception-planning-control pipelines, but their scalability depends on how embodied data can be collected, organized, and reused. This research studies a data-centric framework for industrial embodied intelligence by constructing a logistics data flywheel. Our framework converts daily operations into reusable data assets, uses World Models to generate reliable supervision for long-tail parcel manipulation, and feeds deployment feedback back into policy improvement. As an initial result, \textit{WM-DAgger} introduces a World-Model-based data aggregation framework that synthesizes out-of-distribution recovery data for robust imitation learning. Building on this result, ongoing work explores how large-scale in-the-wild multimodal data, including labeled human demonstrations, unlabeled operational videos, and system-level robot logs, can be aligned for policy learning and transformed into feedback for continual system improvement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
