Bagzel：将机器人数据集构建建模为基于产物的构建流程

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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 20.4 GB dataset). Across dataset sizes from 5.1 to 20.4 GB, Bagzel variants show markedly better scaling behavior than the baseline, especially in warm and incremental modes. Bagzel-xattr provides additional gains, with a mean runtime reduction of 5.9% compared to Bagzel in the input granularity study. Overall, modeling robotics dataset construction as an artifact-based build process substantially reduces dataset update latency while maintaining a deterministic build design that supports reproducibility.

Comments:	Accepted at the 2026 IEEE 22nd International Conference on Automation Science and Engineering (CASE 2026). 7 pages, 6 figures, 2 tables. Code: this https URL
Subjects:	Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as:	arXiv:2606.00162 [cs.RO]
	(or arXiv:2606.00162v2 [cs.RO] for this version)
	https://doi.org/10.48550/arXiv.2606.00162 arXiv-issued DOI via DataCite
Related DOI:	https://doi.org/10.1109/CASE69030.2026.11704392 DOI(s) linking to related resources

Submission history

From: Leon Pohl [view email]
[v1] Fri, 29 May 2026 10:11:45 UTC (160 KB)
[v2] Wed, 7 Oct 2026 15:05:35 UTC (158 KB)