  Autonomous robots often encounter challenging situations where their control
policies fail and an expert human operator must briefly intervene, e.g.,
through teleoperation. In settings where multiple robots act in separate
environments, a single human operator can manage a fleet of robots by
identifying and teleoperating one robot at any given time. The key challenge is
that users have limited attention: as the number of robots increases, users
lose the ability to decide which robot requires teleoperation the most. Our
goal is to automate this decision, thereby enabling users to supervise more
robots than their attention would normally allow for. Our insight is that we
can model the user's choice of which robot to control as an approximately
optimal decision that maximizes the user's utility function. We learn a model
of the user's preferences from observations of the user's choices in easy
settings with a few robots, and use it in challenging settings with more robots
to automatically identify which robot the user would most likely choose to
control, if they were able to evaluate the states of all robots at all times.
We run simulation experiments and a user study with twelve participants that
show our method can be used to assist users in performing a simulated
navigation task. We also run a hardware demonstration that illustrates how our
method can be applied to a real-world mobile robot navigation task.
