  Recent works explore collaboration between humans and teams of robots. These
approaches make sense if the human is already working with the robot team; but
how should robots encourage nearby humans to join their teams in the first
place? Inspired by behavioral economics, we recognize that humans care about
more than just team efficiency -- humans also have biases and expectations for
team 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 be
both legible and fair to the human partner. In this paper we introduce a
bilevel optimization approach that enables robot teams to identify high-level
subtask allocations and low-level trajectories that optimize for legibility,
fairness, or a combination of both objectives. We then test our resulting
algorithm across studies where humans watch or play with robot teams. We find
that our approach to generating legible teams makes the human's role clear, and
that humans typically prefer to join and collaborate with legible teams instead
of teams that only optimize for efficiency. Incorporating fairness alongside
legibility further encourages participation: when humans play with robots, we
find that they prefer (potentially inefficient) teams where the subtasks or
effort are evenly divided. See videos of our studies here
https://youtu.be/cfN7O5na3mg
