Influencing Leading and Following in Human-Robot Teams

Abstract

Recent efforts in human-robot interaction have been focused on modeling and interacting with single human agents. However, when modeling teams of humans, current models are not able to capture underlying emergent dynamics that define group behavior, such as leading and following. We introduce a mathematical framework that enables robots to influence human teams by modeling emergent leading and following behaviors. We tackle the task in two steps. First, we develop a scalable representation of latent leading-following structures by combining model-based methods and data-driven techniques. Second, we optimize for a robot policy that leverages this representation to influence a human team toward a desired outcome. We demonstrate our approach on three tasks where a robot optimizes for changing a leader-follower relationship, distracting a team, and leading a team towards an optimal goal. Our evaluations show that our representation is scalable with different human team sizes, generalizable across different tasks, and can be used to design meaningful robot policies.