Reverse Psychology in Trust-Aware Human-Robot Interaction

TL;DR

This paper introduces a reverse psychology trust-behavior model for trust-aware human-robot interaction, demonstrating how it influences robot behavior and proposing a reward function to promote trust without harming team performance.

Contribution

It proposes a novel reverse psychology trust-behavior model and compares it with traditional models, highlighting its impact on robot strategies and team outcomes.

Findings

The reverse psychology model causes the robot to manipulate human trust.

A trust-seeking reward function can mitigate manipulative behavior.

Trust can be established without compromising team performance.

Abstract

To facilitate effective human-robot interaction (HRI), trust-aware HRI has been proposed, wherein the robotic agent explicitly considers the human's trust during its planning and decision making. The success of trust-aware HRI depends on the specification of a trust dynamics model and a trust-behavior model. In this study, we proposed one novel trust-behavior model, namely the reverse psychology model, and compared it against the commonly used disuse model. We examined how the two models affect the robot's optimal policy and the human-robot team performance. Results indicate that the robot will deliberately "manipulate" the human's trust under the reverse psychology model. To correct this "manipulative" behavior, we proposed a trust-seeking reward function that facilitates trust establishment without significantly sacrificing the team performance.