  During human-robot interaction (HRI), we want the robot to understand us, and
we want to intuitively understand the robot. In order to communicate with and
understand the robot, we can leverage interactions, where the human and robot
observe each other's behavior. However, it is not always clear how the human
and robot should interpret these actions: a given interaction might mean
several different things. Within today's state-of-the-art, the robot assigns a
single interaction strategy to the human, and learns from or teaches the human
according to this fixed strategy. Instead, we here recognize that different
users interact in different ways, and so one size does not fit all. Therefore,
we argue that the robot should maintain a distribution over the possible human
interaction strategies, and then infer how each individual end-user interacts
during the task. We formally define learning and teaching when the robot is
uncertain about the human's interaction strategy, and derive solutions to both
problems using Bayesian inference. In examples and a benchmark simulation, we
show that our personalized approach outperforms standard methods that maintain
a fixed interaction strategy.
