Building Second-Order Mental Models for Human-Robot Interaction
This work addresses improving human-robot interaction by enabling better estimation of human beliefs, though it is incremental as it builds on existing survey methods and applies them in a specific experimental setup.
The paper tackled the problem of inferring human mental models of robots from their actions in a grid-world environment, demonstrating that participants' action choices revealed information about their mental models of a virtual agent.
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.