Building Second-Order Mental Models for Human-Robot Interaction

TL;DR

This paper explores how humans form mental models of robots, proposing a method to infer these models from human actions to enhance human-robot interaction, supported by an online grid-world study.

Contribution

It introduces a novel approach to infer human mental models of robots directly from observed actions, improving understanding of human-robot interaction dynamics.

Findings

Participants' actions reveal their mental models of the virtual agent.

Action choices leak information about human beliefs and intentions.

The method has potential to improve human-robot interaction design.

Abstract

The mental models that humans form of other agents---encapsulating human beliefs about agent goals, intentions, capabilities, and more---create an underlying basis for interaction. These mental models have the potential to affect both the human's decision making during the interaction and the human's subjective assessment of the interaction. In this paper, we surveyed existing methods for modeling how humans view robots, then identified a potential method for improving these estimates through inferring a human's model of a robot agent directly from their actions. Then, we conducted an online study to collect data in a grid-world environment involving humans moving an avatar past a virtual agent. Through our analysis, we demonstrated that participants' action choices leaked information about their mental models of a virtual agent. We conclude by discussing the implications of these findings and the potential for such a method to improve human-robot interactions.