A Theoretical Framework for Large-Scale Human-Robot Interaction with Groups of Learning Agents

Recent advances in robot capabilities have led to a growing consensus that robots will eventually be deployed at scale across numerous application domains. An important open question is how humans and robots will adapt to one another over time. In this paper, we introduce the model-based Theoretical Human-Robot Scenarios (THuS) framework, capable of elucidating the interactions between large groups of humans and learning robots. We formally establish THuS, and consider its application to a human-robot variant of the n-player coordination game, demonstrating the power of the theoretical framework as a tool to qualitatively understand and quantitatively compare HRI scenarios that involve different agent types. We also discuss the framework's limitations and potential. Our work provides the HRI community with a versatile tool that permits first-cut insights into large-scale HRI scenarios that are too costly or challenging to carry out in simulations or in the real-world.