用可追溯性技术提升机器人仿真验证的可复现性
Replicable Simulation-Based Robot Validation through Provenance
- 在仿真测试流程中嵌入数据溯源与元数据记录
- 为移动机器人导航数据集添加结构化溯源信息
- 适合关注验证可复现性的机器人研发团队
机器人行为常通过仿真测试验证，但此类测试的可复现性高度依赖于测试配置、执行和后处理过程的透明记录。本文提出，结合数据溯源与FAIR原则（可发现性、可访问性、可互操作性、可重用性），通过显式追踪产物间关联并附加关于文件来源和关键设计决策的机器可读元数据，可解决该问题。更重要的是，溯源与元数据不能仅作为最终数据集的附加内容，而应集成至生成数据的测试流程中，以实现端到端证据重建。我们通过扩展现有仿真测试框架，加入溯源追踪与元数据收集机制，并以此丰富了一个移动机器人导航数据集，使其具备结构化溯源与符合FAIR原则的元数据。最后，讨论了集成过程中遇到的挑战，如术语对齐、属性选择及领域标准采纳，并提供可操作建议，推动机器人验证工作流向溯源中心化、FAIR元数据方向演进。
原文摘要 · Abstract (English)
Robot behavior is often validated through simulation-based testing, yet the replicability of such campaigns depends critically on transparent documentation of how tests are configured, executed, and post-processed. We argue that data provenance, coupled with the FAIR principles (findability, accessibility, interoperability, and reusability), addresses this gap by explicitly tracking links between artifacts and by attaching machine-readable metadata about file origins and key design decisions. Moreover, provenance and metadata cannot be treated as an afterthought confined to final datasets; they must be integrated into the testing processes that generate those datasets so that evidence can be reconstructed end-to-end. We demonstrate this by augmenting an existing simulation-based testing framework with provenance tracking and metadata collection mechanisms, and by using these extensions to enrich a mobile robot navigation dataset with structured provenance and FAIR-aligned metadata. Finally, we discuss obstacles encountered in this integration -- such as vocabulary alignment, attribute selection, and adoption of domain standards -- and provide actionable recommendations for implementing provenance-centric, FAIR metadata in robotics validation workflows.
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