Trust-Aware Planning: Modeling Trust Evolution in Iterated Human-Robot Interaction

TL;DR

This paper introduces a computational model for trust management in human-robot teams, enabling robots to adapt their behavior based on trust levels to improve cooperation and team success.

Contribution

It presents a novel trust-aware planning framework that incorporates human trust expectations into robot decision-making in iterative interactions.

Findings

The model effectively maintains trust over multiple interactions.

Robots using the model reduce unnecessary explanations.

Human subjects preferred trust-aware robot behaviors.

Abstract

Trust between team members is an essential requirement for any successful cooperation. Thus, engendering and maintaining the fellow team members' trust becomes a central responsibility for any member trying to not only successfully participate in the task but to ensure the team achieves its goals. The problem of trust management is particularly challenging in mixed human-robot teams where the human and the robot may have different models about the task at hand and thus may have different expectations regarding the current course of action, thereby forcing the robot to focus on the costly explicable behavior. We propose a computational model for capturing and modulating trust in such iterated human-robot interaction settings, where the human adopts a supervisory role. In our model, the robot integrates human's trust and their expectations about the robot into its planning process to build and maintain trust over the interaction horizon. By establishing the required level of trust, the robot can focus on maximizing the team goal by eschewing explicit explanatory or explicable behavior without worrying about the human supervisor monitoring and intervening to stop behaviors they may not necessarily understand. We model this reasoning about trust levels as a meta reasoning process over individual planning tasks. We additionally validate our model through a human subject experiment.