Wait, That Feels Familiar: Learning to Extrapolate Human Preferences for
  Preference Aligned Path Planning
Haresh Karnan Elvin Yang Garrett Warnell Joydeep Biswas Peter Stone
Autonomous mobility tasks such as lastmile delivery require reasoning about
operator indicated preferences over terrains on which the robot should navigate
to ensure both robot safety and mission success. However, coping with out of
distribution data from novel terrains or appearance changes due to lighting
variations remains a fundamental problem in visual terrain adaptive navigation.
Existing solutions either require labor intensive manual data recollection and
labeling or use handcoded reward functions that may not align with operator
preferences. In this work, we posit that operator preferences for visually
novel terrains, which the robot should adhere to, can often be extrapolated
from established terrain references within the inertial, proprioceptive, and
tactile domain. Leveraging this insight, we introduce Preference extrApolation
for Terrain awarE Robot Navigation, PATERN, a novel framework for extrapolating
operator terrain preferences for visual navigation. PATERN learns to map
inertial, proprioceptive, tactile measurements from the robots observations to
a representation space and performs nearest neighbor search in this space to
estimate operator preferences over novel terrains. Through physical robot
experiments in outdoor environments, we assess PATERNs capability to
extrapolate preferences and generalize to novel terrains and challenging
lighting conditions. Compared to baseline approaches, our findings indicate
that PATERN robustly generalizes to diverse terrains and varied lighting
conditions, while navigating in a preference aligned manner.