We introduce Kleinkram, a free and open-source system designed to solve the challenge of managing massive, unstructured robotic datasets. Designed as a modular, on-premises cloud solution, Kleinkram enables scalable storage, indexing, and sharing of datasets, ranging from individual experiments to large-scale research collections. Kleinkram natively integrates with standard formats such as ROS bags and MCAP and utilises S3-compatible storage for flexibility. Beyond storage, Kleinkram features an integrated "Action Runner" that executes customizable Docker-based workflows for data validation, curation, and benchmarking. Kleinkram has successfully managed over 30 TB of data from diverse robotic systems, streamlining the research lifecycle through a modern web interface and a robust Command Line Interface (CLI).
@article{arxiv.2511.20492,
title = {Kleinkram: Open Robotic Data Management},
author = {Cyrill Püntener and Johann Schwabe and Dominique Garmier and Jonas Frey and Marco Hutter},
journal= {arXiv preprint arXiv:2511.20492},
year = {2025}
}
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for associated source code, see https://github.com/leggedrobotics/kleinkram