  To effectively assist human workers in assembly tasks a robot must
proactively offer support by inferring their preferences in sequencing the task
actions. Previous work has focused on learning the dominant preferences of
human workers for simple tasks largely based on their intended goal. However,
people may have preferences at different resolutions: they may share the same
high-level preference for the order of the sub-tasks but differ in the sequence
of individual actions. We propose a two-stage approach for learning and
inferring the preferences of human operators based on the sequence of sub-tasks
and actions. We conduct an IKEA assembly study and demonstrate how our approach
is able to learn the dominant preferences in a complex task. We show that our
approach improves the prediction of human actions through cross-validation.
Lastly, we show that our two-stage approach improves the efficiency of task
execution in an online experiment, and demonstrate its applicability in a
real-world robot-assisted IKEA assembly.
