Search papers, labs, and topics across Lattice.
This paper addresses the replicability crisis in robot simulation-based testing by advocating for the integration of data provenance and FAIR principles. They augment an existing simulation framework with provenance tracking to link artifacts and attach machine-readable metadata about file origins and design decisions. The authors demonstrate this approach by enriching a mobile robot navigation dataset with structured provenance and FAIR-aligned metadata, highlighting challenges and providing recommendations for implementation.
Robot simulation validation is often irreproducible, but this work shows how provenance tracking and FAIR principles can make it robust.
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.
