  Robots often need to convey information to human users. For example, robots
can leverage visual, auditory, and haptic interfaces to display their intent or
express their internal state. In some scenarios there are socially agreed upon
conventions for what these signals mean: e.g., a red light indicates an
autonomous car is slowing down. But as robots develop new capabilities and seek
to convey more complex data, the meaning behind their signals is not always
mutually understood: one user might think a flashing light indicates the
autonomous car is an aggressive driver, while another user might think the same
signal means the autonomous car is defensive. In this paper we enable robots to
adapt their interfaces to the current user so that the human's personalized
interpretation is aligned with the robot's meaning. We start with an
information theoretic end-to-end approach, which automatically tunes the
interface policy to optimize the correlation between human and robot. But to
ensure that this learning policy is intuitive -- and to accelerate how quickly
the interface adapts to the human -- we recognize that humans have priors over
how interfaces should function. For instance, humans expect interface signals
to be proportional and convex. Our approach biases the robot's interface
towards these priors, resulting in signals that are adapted to the current user
while still following social expectations. Our simulations and user study
results across $15$ participants suggest that these priors improve
robot-to-human communication. See videos here: https://youtu.be/Re3OLg57hp8
