Modeling Trust Dynamics in Robot-Assisted Delivery: Impact of Trust Repair Strategies

TL;DR

This paper models how human trust in robot-assisted delivery changes with robot performance and repair strategies, using a Hidden Markov Model to predict trust dynamics and inform better trust management.

Contribution

It introduces an Input-Output Hidden Markov Model to accurately capture and predict human trust dynamics in robot-assisted delivery scenarios, incorporating various trust repair strategies.

Findings

Long explanations effectively repair trust after failures.

Denial strategies best prevent trust loss.

Trust estimates align with self-reported trust levels.

Abstract

With increasing efficiency and reliability, autonomous systems are becoming valuable assistants to humans in various tasks. In the context of robot-assisted delivery, we investigate how robot performance and trust repair strategies impact human trust. In this task, while handling a secondary task, humans can choose to either send the robot to deliver autonomously or manually control it. The trust repair strategies examined include short and long explanations, apology and promise, and denial. Using data from human participants, we model human behavior using an Input-Output Hidden Markov Model (IOHMM) to capture the dynamics of trust and human action probabilities. Our findings indicate that humans are more likely to deploy the robot autonomously when their trust is high. Furthermore, state transition estimates show that long explanations are the most effective at repairing trust following a failure, while denial is most effective at preventing trust loss. We also demonstrate that the trust estimates generated by our model are isomorphic to self-reported trust values, making them interpretable. This model lays the groundwork for developing optimal policies that facilitate real-time adjustment of human trust in autonomous systems.