Gazing at Failure: Investigating Human Gaze in Response to Robot Failure in Collaborative Tasks

TL;DR

This study explores how human gaze behavior reflects robot failures during collaborative tasks, revealing that gaze patterns vary with failure type and timing, which can inform failure detection and recovery strategies.

Contribution

It demonstrates that human gaze dynamics can reliably indicate different types of robot failures and their occurrence timing during collaborative tasks.

Findings

Executional failures increase gaze shifts and focus on the robot.

Decisional failures reduce entropy in gaze transitions.

Gaze patterns vary significantly with failure type and timing.

Abstract

Robots are prone to making errors, which can negatively impact their credibility as teammates during collaborative tasks with human users. Detecting and recovering from these failures is crucial for maintaining effective level of trust from users. However, robots may fail without being aware of it. One way to detect such failures could be by analysing humans' non-verbal behaviours and reactions to failures. This study investigates how human gaze dynamics can signal a robot's failure and examines how different types of failures affect people's perception of robot. We conducted a user study with 27 participants collaborating with a robotic mobile manipulator to solve tangram puzzles. The robot was programmed to experience two types of failures -- executional and decisional -- occurring either at the beginning or end of the task, with or without acknowledgement of the failure. Our findings reveal that the type and timing of the robot's failure significantly affect participants' gaze behaviour and perception of the robot. Specifically, executional failures led to more gaze shifts and increased focus on the robot, while decisional failures resulted in lower entropy in gaze transitions among areas of interest, particularly when the failure occurred at the end of the task. These results highlight that gaze can serve as a reliable indicator of robot failures and their types, and could also be used to predict the appropriate recovery actions.