Robotic Journaling: Translating Raw Robot Logs into Structured Narratives to Model Trust in Heterogeneous Human-Machine Teams

Abstract

ABSTRACT: Robot-generated logs offer the most comprehensive record of machine behavior and human-machine interactions in field settings, yet their technical complexity renders them inaccessible to human factors researchers. This paper introduces Robotic Journaling, a systematic four-step methodology for transforming technical robot logs into analyzable narratives suitable for rigorous qualitative analysis. The method comprises: (1) systematic log collection, (2) collaborative development of translation codebooks with operators and engineers, (3) transformation of technical logs into plain language narratives, and (4) application of chosen analytical approaches to translated data. We demonstrate this methodology through its application to the DARPA Subterranean Challenge, where NASA JPL’s CoSTAR team operated a heterogeneous fleet of autonomous robots in underground environments. Through Robotic Journaling, we translated 536 pages of fragmented logs from 151 days of field testing into 228 pages of coherent narratives. While we use Grounded Theory analysis of trust dynamics to illustrate how the translated narratives enable qualitative research, this paper focuses specifically on detailing the Robotic Journaling methodology itself rather than presenting analytical findings. This methodology addresses a critical gap in human-machine teaming research by making field-generated data accessible when direct observation is impossible or insufficient particularly vital in high-stakes Real users, Real systems, Real consequences (R3) environments like space exploration, disaster response, and military operations. The method is domain-agnostic and transferable to any research question that could benefit from systematic analysis of robot-generated logs.

Keywords: Human-Machine Teams (HMTs), Trust in Autonomy, Robotic Journaling, Real-World Data (R3), Human-Robot Communication

DOI: 10.54941/ahfe1006939