Influencing leading and following in human–robot teams

Roles such as leading and following can emerge naturally in human groups. However, in human–robot teams, such roles are often predefined due to the difficulty of scalably learning and adapting to them. In this work, we enable a robot to efficiently learn how group dynamics emerge and evolve in human teams and we leverage this understanding to plan for influencing actions for autonomous robots that guide the team toward achieving a common goal. We first develop an effective and concise representation of group dynamics, such as leading and following, by enforcing a graph structure while learning the weights of the edges corresponding to one-to-one relationships between the agents. We then develop an optimization-based robot policy that leverages this graph representation to attain an objective by influencing a human team. We apply our framework to two types of group dynamics, leading-following and predator–prey, and show that our structured representation is scalable with different human team sizes and also generalizable across different tasks. We also show that robots that utilize this representation are able to successfully influence a group to achieve various goals compared to robots that do not have access to these graph representations (Parts of this work has been published at Robotics: Science and Systems (RSS) (Kwon et al. in Proceedings of robotics: science and systems (RSS), 2019. https://doi.org/10.15607/rss.2019.xv.075 ).