Trust Calibration and Trust Respect: A Method for Building Team Cohesion in Human Robot Teams

TL;DR

This paper introduces a method for improving team cohesion in human-robot teams by calibrating or respecting human trust based on robot feedback and human emotional cues, enhancing safety and performance.

Contribution

It proposes a novel approach for trust calibration and respect in human-robot teams, incorporating trust calibration cues and human emotional assessment to improve team dynamics.

Findings

Trust calibration cues effectively influence human trust levels.

Robots can assess when to calibrate or respect human trust based on feedback and emotional cues.

Improved trust management enhances team cohesion and safety.

Abstract

Recent advances in the areas of human-robot interaction (HRI) and robot autonomy are changing the world. Today robots are used in a variety of applications. People and robots work together in human autonomous teams (HATs) to accomplish tasks that, separately, cannot be easily accomplished. Trust between robots and humans in HATs is vital to task completion and effective team cohesion. For optimal performance and safety of human operators in HRI, human trust should be adjusted to the actual performance and reliability of the robotic system. The cost of poor trust calibration in HRI, is at a minimum, low performance, and at higher levels it causes human injury or critical task failures. While the role of trust calibration is vital to team cohesion it is also important for a robot to be able to assess whether or not a human is exhibiting signs of mistrust due to some other factor such as anger, distraction or frustration. In these situations the robot chooses not to calibrate trust, instead the robot chooses to respect trust. The decision to respect trust is determined by the robots knowledge of whether or not a human should trust the robot based on its actions(successes and failures) and its feedback to the human. We show that the feedback in the form of trust calibration cues(TCCs) can effectively change the trust level in humans. This information is potentially useful in aiding a robot it its decision to respect trust.