aiqbench analysis
This research addresses a critical engineering bottleneck in robotics by reframing dataset construction as an artifact-based build process, significantly reducing iteration latency for large-scale multimodal data pipelines. By leveraging dependency graphs similar to Bazel, the proposed method enables reproducible and incremental generation of ML datasets, which is essential for accelerating training cycles in autonomous systems. While the core innovation lies in software infrastructure, its primary impact is enabling more efficient development workflows for machine learning models applied to robotics.
Modeling Robotics Dataset Construction as an Artifact-Based Build Process
arXiv:2606.00162v2 Announce Type: replace-cross 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.
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.
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
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
arXiv:2606.00162 [cs.RO]
(or arXiv:2606.00162v2 [cs.RO] for this version)
https://doi.org/10.1109/CASE69030.2026.11704392 DOI(s) linking to related resources
Full text under the publisher's open licence · 225 words
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