LATIDIA · Robótica

Modelado de la construcción de conjuntos de datos de robótica como un proceso de construcción basado en artefactos

arXiv: 2606.00162v2Tipo de anuncio: reemplazar Resumen: Los sistemas robóticos generan grandes volúmenes de datos de sensores multimodales, pero la conversión de las grabaciones de bolsas ROS en conjuntos de datos de aprendizaje automático a menudo se maneja de forma secuencial ad hoc

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arXiv:2606.00162v2 Announce Type: replace Abstract: Robotic systems generate large volumes of multimodal sensor data, but converting ROS bag recordings into machine learning datasets is often handled by ad hoc sequential scripts, creating engineering overhead and slow iteration cycles. We model dataset construction as an artifact-based build process over a dependency graph and implement this approach in Bagzel, an open-source Bazel extension for reproducible, incremental dataset generation (including nuScenes-format export). We compare Bagzel and Bagzel-xattr (server-side digest management) against a sequential rosbag2nuscenes baseline. Bagzel reduces runtime in all evaluated execution modes, with the largest gains in iterative workflows (up to 386.26x in warm builds and 7.21x in incremental builds on a