見慣れない地形でも好みを推測：人間の選好に沿った経路計画のための選好外挿学習
Wait, That Feels Familiar: Learning to Extrapolate Human Preferences for Preference Aligned Path Planning
慣性・固有感覚・触覚の計測から人間の地形選好を表現空間に写像し、視覚的に未知の地形でも最近傍探索で選好を外挿する枠組みPATERNを提案し、屋外実験で照明変化や新規地形への頑健な汎化を示した。
著者: Haresh Karnan, Elvin Yang, Garrett Warnell, Joydeep Biswas, Peter Stone
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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.
