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STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience
Terrain awareness, i. e., the ability to identify and distinguish different types of terrain, is a critical ability that robots must have to succeed at autonomous off-road navigation.
Wait, That Feels Familiar: Learning to Extrapolate Human Preferences for Preference Aligned Path Planning
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.
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