构建物流机器人数据飞轮，实现智能体持续进化。
Towards a Data Flywheel for Embodied Intelligence in Logistics
- 以世界模型生成长尾任务监督信号，提升模仿学习鲁棒性。
- 通过部署反馈闭环优化策略，实现数据资产持续积累与复用。
- 适合关注工业级具身智能落地的研究者与工程师。
具身智能正从实验室走向工业应用，物流领域是关键场景。基于学习的策略为超越传统感知-规划-控制范式提供了可能，但其可扩展性依赖于具身数据的采集、组织与复用。本文提出一种以数据为中心的工业具身智能框架，构建物流数据飞轮：将日常运营转化为可复用的数据资产，利用世界模型（World Models）生成长尾包裹操作的可靠监督信号，并将部署反馈回流至策略优化。初步成果提出 extit{WM-DAgger}，一种基于世界模型的数据聚合框架，合成分布外恢复数据以增强模仿学习鲁棒性。后续工作探索如何对大规模真实场景多模态数据（包括标注的人类示范、未标注的操作视频和系统级机器人日志）进行对齐，用于策略学习，并转化为持续改进系统的反馈。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。