Interactive Lab Notebooks for Robotics Researchers (2405.08200v1)
Abstract: Interactive notebooks, such as Jupyter, have revolutionized the field of data science by providing an integrated environment for data, code, and documentation. However, their adoption by robotics researchers and model developers has been limited. This study investigates the logging and record-keeping practices of robotics researchers, drawing parallels to the pre-interactive notebook era of data science. Through interviews with robotics researchers, we identified the reliance on diverse and often incompatible tools for managing experimental data, leading to challenges in reproducibility and data traceability. Our findings reveal that robotics researchers can benefit from a specialized version of interactive notebooks that supports comprehensive data entry, continuous context capture, and agile data staging. We propose extending interactive notebooks to better serve the needs of robotics researchers by integrating features akin to traditional lab notebooks. This adaptation aims to enhance the organization, analysis, and reproducibility of experimental data in robotics, fostering a more streamlined and efficient research workflow.
- Software engineering for machine learning: A case study. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pages 291–300. IEEE, 2019.
- Hindsight logging for model training. Proceedings of the VLDB Endowment, 14(4):682–693, 2020.
- Managing messes in computational notebooks. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pages 1–12, 2019.
- Designing for the" universe of one" personalized interactive media systems for people with the severe cognitive impairment associated with rett syndrome. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pages 2137–2148, 2017.
- Enterprise data analysis and visualization: An interview study. IEEE transactions on visualization and computer graphics, 18(12):2917–2926, 2012.
- Interactions for untangling messy history in a computational notebook. In 2018 IEEE symposium on visual languages and human-centric computing (VL/HCC), pages 147–155. IEEE, 2018.
- M. Muller. Curiosity, creativity, and surprise as analytic tools: Grounded theory method. In Ways of Knowing in HCI, pages 25–48. Springer, 2014.
- F. Perez and B. E. Granger. Project jupyter: Computational narratives as the engine of collaborative data science. Retrieved September, 11(207):108, 2015.
- "we have no idea how models will behave in production until production": How engineers operationalize machine learning. Proceedings of the ACM on Human-Computer Interaction, 8(CSCW1):1–34, 2024.
- Rolando Garcia (7 papers)