arXiv:2606.00162v1 Announce Type: 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) agai
The increasing complexity and volume of multimodal sensor data from robotic systems, particularly for machine learning, demand more robust and reproducible dataset construction methods.
Efficient and reproducible dataset generation is fundamental for accelerating progress in AI and robotics, reducing development cycles, and improving model reliability.
Dataset construction in robotics, traditionally ad hoc, is shifting towards structured, artifact-based build processes, improving iteration speed and reliability.
- · Robotics developers
- · AI researchers
- · Companies building robotic systems
- · Open-source tool developers
- · Manual data engineering teams
- · Proprietary, inflexible data processing pipelines
Faster development and deployment of robotic AI models due to improved data pipelines.
Increased adoption of standardized data formats and tools across the robotics and AI industries.
Potentially lower barriers to entry for new robotics AI developers, decentralizing innovation.
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Read at arXiv cs.LG