  Preference learning has long been studied in Human-Robot Interaction (HRI) in
order to adapt robot behavior to specific user needs and desires. Typically,
human preferences are modeled as a scalar function; however, such a formulation
confounds critical considerations on how the robot should behave for a given
task, with desired -- but not required -- robot behavior. In this work, we
distinguish between such required and desired robot behavior by leveraging a
planning framework. Specifically, we propose a novel problem formulation for
preference learning in HRI where various types of human preferences are encoded
as soft planning constraints. Then, we explore a data-driven method to enable a
robot to infer preferences by querying users, which we instantiate in
rearrangement tasks in the Habitat 2.0 simulator. We show that the proposed
approach is promising at inferring three types of preferences even under
varying levels of noise in simulated user choices between potential robot
behaviors. Our contributions open up doors to adaptable planning-based robot
behavior in the future.
