Anytime Trust Rating Dynamics in a Human-Robot Interaction Task
Jason Dekarske, Gregory Bales, Zhaodan Kong, Sanjay Joshi

TL;DR
This study models the timing of trust ratings in human-robot interaction, revealing that trust is influenced more by task progress than robot performance, and introduces a minimally obtrusive trust measurement method.
Contribution
It presents a Cox model for trust rating timing and demonstrates that trust ratings are primarily driven by task progress, not robot performance.
Findings
Trust ratings are mainly influenced by task progress.
Robot performance has little effect on trust rating timing.
The proposed method allows trust measurement with minimal distraction.
Abstract
Objective We model factors contributing to rating timing for a single-dimensional, any-time trust in robotics measure. Background Many studies view trust as a slow-changing value after subjects complete a trial or at regular intervals. Trust is a multifaceted concept that can be measured simultaneously with a human-robot interaction. Method 65 subjects commanded a remote robot arm in a simulated space station. The robot picked and placed stowage commanded by the subject, but the robot's performance varied from trial to trial. Subjects rated their trust on a non-obtrusive trust slider at any time throughout the experiment. Results A Cox Proportional Hazards Model described the time it took subjects to rate their trust in the robot. A retrospective survey indicated that subjects based their trust on the robot's performance or outcome of the task. Strong covariates representing the…
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Taxonomy
TopicsReinforcement Learning in Robotics · Fault Detection and Control Systems · Explainable Artificial Intelligence (XAI)
