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Visual Representation Learning for Preference-Aware Path Planning
We introduce Visual Representation Learning for Preference-Aware Path Planning (VRL-PAP), an alternative approach that overcomes all three limitations: VRL-PAP leverages unlabeled human demonstrations of navigation to autonomously generate triplets for learning visual representations of terrain that are viewpoint invariant and encode terrain types in a continuous representation space.
Algorithmic Probability-guided Supervised Machine Learning on Non-differentiable Spaces
We show how complexity theory can be introduced in machine learning to help bring together apparently disparate areas of current research.
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