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Recent works explore collaboration between humans and teams of robots. Theseapproaches make sense if the human is already working with the robot team; buthow should robots encourage nearby humans to join their teams in the firstplace? Inspired by behavioral economics, we recognize that humans care aboutmore than just team efficiency -- humans also have biases and expectations forteam dynamics. Our hypothesis is that the way inclusive robots divide the task(i.e., how the robots split a larger task into subtask allocations) should beboth legible and fair to the human partner. In this paper we introduce abilevel optimization approach that enables robot teams to identify high-levelsubtask allocations and low-level trajectories that optimize for legibility,fairness, or a combination of both objectives. We then test our resultingalgorithm across studies where humans watch or play with robot teams. We findthat our approach to generating legible teams makes the human's role clear, andthat humans typically prefer to join and collaborate with legible teams insteadof teams that only optimize for efficiency. Incorporating fairness alongsidelegibility further encourages participation: when humans play with robots, wefind that they prefer (potentially inefficient) teams where the subtasks oreffort are evenly divided. See videos of our studies herehttps://youtu.be/cfN7O5na3mg

Encouraging Human Interaction with Robot Teams: Legible and Fair Subtask Allocations