Learning from Richer Human Guidance: Augmenting Comparison-Based
  Learning with Feature Queries
  We focus on learning the desired objective function for a robot. Although
trajectory demonstrations can be very informative of the desired objective,
they can also be difficult for users to provide. Answers to comparison queries,
asking which of two trajectories is preferable, are much easier for users, and
have emerged as an effective alternative. Unfortunately, comparisons are far
less informative. We propose that there is much richer information that users
can easily provide and that robots ought to leverage. We focus on augmenting
comparisons with feature queries, and introduce a unified formalism for
treating all answers as observations about the true desired reward. We derive
an active query selection algorithm, and test these queries in simulation and
on real users. We find that richer, feature-augmented queries can extract more
information faster, leading to robots that better match user preferences in
their behavior.
