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Interactive Lab Notebooks for Robotics Researchers

Rolando Garcia

TL;DR

This paper investigates why robotics researchers underutilize interactive notebooks and how to adapt notebooks to lab-style workflows. Using Grounded Theory on semi-structured interviews with UC Berkeley robotics researchers, it shows that their data practices are fragmented across heterogeneous tools, risking provenance loss. The authors propose reframing data science as encompassing traditional lab-notebook activities and outline a design path for next-generation notebooks that merge data entry, context capture, and agile data staging. The work suggests practical routes to improve reproducibility, experiment design, and documentation in robotics research by extending interactive notebooks toward lab-notebook semantics.

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.

Interactive Lab Notebooks for Robotics Researchers

TL;DR

This paper investigates why robotics researchers underutilize interactive notebooks and how to adapt notebooks to lab-style workflows. Using Grounded Theory on semi-structured interviews with UC Berkeley robotics researchers, it shows that their data practices are fragmented across heterogeneous tools, risking provenance loss. The authors propose reframing data science as encompassing traditional lab-notebook activities and outline a design path for next-generation notebooks that merge data entry, context capture, and agile data staging. The work suggests practical routes to improve reproducibility, experiment design, and documentation in robotics research by extending interactive notebooks toward lab-notebook semantics.

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.
Paper Structure (15 sections, 1 figure, 1 table)

This paper contains 15 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Contributions
  3. Theoretical Framework
  4. Methods
  5. Recruitment
  6. Semi-structured Interview
  7. Transcript Coding and Post-Processing
  8. Thematic Analysis
  9. Summary of Findings
  10. Data Staging, Backlogs, and Journaling: Jobs for Scientific Lab Notebooks
  11. Data Staging
  12. Backlog (Sprint Planning)
  13. Journaling
  14. Reflections
  15. Acknowledgements

Figures (1)

  • Figure 1: Data matrix of themes in interview transcripts. Each element in a cell corresponds to a theme, the bulleted codes correspond to transcript block tags. Themes are grouped by next-gen interactive notebook components.