构建跨组织机器人数据到知识的智能管道，实现协同研发与控制模型训练。
Demonstrating Data-to-Knowledge Pipelines for Connecting Production Sites in the World Wide Lab
- 基于数字影子网络构建数据到知识的通用流程
- 在湖仓架构中统一管理多机构机器人轨迹数据并语义标注
- 动态查询匹配数据训练反向动力学基础模型，支持远程控制优化
生产数字化转型需要新的数据集成与存储方法，以及贯穿研发、生产、使用全周期的决策支持系统。本文提出面向生产的数据到知识（及知识到数据）管道，作为建立在数字影子网络（增强版数字孪生概念）上的通用框架。通过概念验证展示：1）在湖仓架构中捕获并语义标注来自不同组织、不同应用场景下多个独立机器人的轨迹数据；2）独立流程动态查询匹配数据，用于训练用于机器人控制的反向动力学基础模型。文章讨论该方法的挑战与优势，表明其在世界范围实验室（World Wide Lab）研究愿景下可带来效率提升与工业规模化潜力。
原文摘要 · Abstract (English)
The digital transformation of production requires new methods of data integration and storage, as well as decision making and support systems that work vertically and horizontally throughout the development, production, and use cycle. In this paper, we propose Data-to-Knowledge (and Knowledge-to-Data) pipelines for production as a universal concept building on a network of Digital Shadows (a concept augmenting Digital Twins). We show a proof of concept that builds on and bridges existing infrastructure to 1) capture and semantically annotates trajectory data from multiple similar but independent robots in different organisations and use cases in a data lakehouse and 2) an independent process that dynamically queries matching data for training an inverse dynamic foundation model for robotic control. The article discusses the challenges and benefits of this approach and how Data-to-Knowledge pipelines contribute efficiency gains and industrial scalability in a World Wide Lab as a research outlook.
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