Effective multi-agent teaming requires knowledgeable robots to have the capability of influencing their teammates. Robots are able to possess information that their human and other agent teammates do not, such as by scouting ahead in dangerous areas. To work as an effective team, robots must be able to influence their teammates when necessary and adapt to changing situations in order to move to goal positions that only they may be aware of, while remaining connected as a team. In this paper, we propose the problem of multiple robot teammates tasked with leading a multi-agent team to multiple goal positions while maintaining the ability to communicate with one another. We define utilities of making progress towards goals, maintaining communications with followers, and maintaining communications with fellow leaders. In addition, we introduce a novel regularized optimization formulation that balances these utilities and utilizes structured sparsity inducing norms to focus the leaders' attention on specific goals and followers over time. The dynamically learned utility allows our approach to generate an action for each leader at each time step, which allows the leaders to reach goals without sacrificing communication. We show through extensive synthetic and high-fidelity simulations that our method effectively enables multiple robotic leaders to guide a multi-agent team to different goals while maintaining communication.
