  Fundamental to robotics is the debate between model-based and model-free
learning: should the robot build an explicit model of the world, or learn a
policy directly? In the context of HRI, part of the world to be modeled is the
human. One option is for the robot to treat the human as a black box and learn
a policy for how they act directly. But it can also model the human as an
agent, and rely on a "theory of mind" to guide or bias the learning (grey box).
We contribute a characterization of the performance of these methods for an
autonomous driving task under the optimistic case of having an ideal theory of
mind, as well as under different scenarios in which the assumptions behind the
robot's theory of mind for the human are wrong, as they inevitably will be in
practice.
