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.

翻译：交互式笔记本（如Jupyter）通过为数据、代码和文档提供集成环境，彻底改变了数据科学领域。然而，机器人研究人员和模型开发者对其采用仍十分有限。本研究调查了机器人研究人员的日志记录与数据保存实践，并将其与数据科学在交互式笔记本出现之前的时代进行类比。通过对机器人研究人员的访谈，我们发现他们依赖多样且往往互不兼容的工具来管理实验数据，这导致了可重复性和数据可溯源方面的挑战。研究结果表明，机器人研究人员可以从一种支持全面数据录入、持续语境捕获和敏捷数据分阶段的专用交互式笔记本中受益。我们建议通过集成传统实验笔记本的功能来扩展交互式笔记本，以更好地满足机器人研究人员的需求。这种改进旨在提升机器人领域实验数据的组织性、分析能力和可重复性，从而促进更精简高效的研究流程。