Robot Capability and Intention in Trust-based Decisions across Tasks

TL;DR

This study investigates how human mental models of robot capability and intention influence trust and delegation decisions across various tasks, highlighting the importance of multi-faceted trust calibration for effective human-robot collaboration.

Contribution

It provides empirical evidence that human trust decisions are based on integrated mental models of robot intention, capability, and overall trust, across different task contexts.

Findings

Trust correlates with perceived robot capability and intention.

Overall trust alone does not predict delegation decisions.

Humans use multi-faceted mental models for trust calibration.

Abstract

In this paper, we present results from a human-subject study designed to explore two facets of human mental models of robots---inferred capability and intention---and their relationship to overall trust and eventual decisions. In particular, we examine delegation situations characterized by uncertainty, and explore how inferred capability and intention are applied across different tasks. We develop an online survey where human participants decide whether to delegate control to a simulated UAV agent. Our study shows that human estimations of robot capability and intent correlate strongly with overall self-reported trust. However, overall trust is not independently sufficient to determine whether a human will decide to trust (delegate) a given task to a robot. Instead, our study reveals that estimations of robot intention, capability, and overall trust are integrated when deciding to delegate. From a broader perspective, these results suggest that calibrating overall trust alone is insufficient; to make correct decisions, humans need (and use) multi-faceted mental models when collaborating with robots across multiple contexts.