Replicable Simulation-Based Robot Validation through Provenance

• URL: http://arxiv.org/abs/2605.29973v2
• Date: Fri, 29 May 2026 12:50:50 GMT
• Title: Replicable Simulation-Based Robot Validation through Provenance
• Abstract summary: We argue that data provenance, coupled with the FAIR principles, addresses the gap by explicitly tracking links between artifacts and by attaching machine-readable metadata about file origins and key design decisions.<n>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.
• Score: 4.63732827131233
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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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