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Autonomous mobility tasks such as lastmile delivery require reasoning aboutoperator indicated preferences over terrains on which the robot should navigateto ensure both robot safety and mission success. However, coping with out ofdistribution data from novel terrains or appearance changes due to lightingvariations remains a fundamental problem in visual terrain adaptive navigation.Existing solutions either require labor intensive manual data recollection andlabeling or use handcoded reward functions that may not align with operatorpreferences. In this work, we posit that operator preferences for visuallynovel terrains, which the robot should adhere to, can often be extrapolatedfrom established terrain references within the inertial, proprioceptive, andtactile domain. Leveraging this insight, we introduce Preference extrApolationfor Terrain awarE Robot Navigation, PATERN, a novel framework for extrapolatingoperator terrain preferences for visual navigation. PATERN learns to mapinertial, proprioceptive, tactile measurements from the robots observations toa representation space and performs nearest neighbor search in this space toestimate operator preferences over novel terrains. Through physical robotexperiments in outdoor environments, we assess PATERNs capability toextrapolate preferences and generalize to novel terrains and challenginglighting conditions. Compared to baseline approaches, our findings indicatethat PATERN robustly generalizes to diverse terrains and varied lightingconditions, while navigating in a preference aligned manner.

Wait, That Feels Familiar: Learning to Extrapolate Human Preferences for Preference Aligned Path Planning