Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Visual Representation Learning for Preference-Aware Path Planning   Autonomous mobile robots deployed in outdoor environments must reason about
different types of terrain for both safety (e.g., prefer dirt over mud) and
deployer preferences (e.g., prefer dirt path over flower beds). Most existing
solutions to this preference-aware path planning problem use semantic
segmentation to classify terrain types from camera images, and then ascribe
costs to each type. Unfortunately, there are three key limitations of such
approaches -- they 1) require pre-enumeration of the discrete terrain types, 2)
are unable to handle hybrid terrain types (e.g., grassy dirt), and 3) require
expensive labelled data to train visual semantic segmentation. 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. The learned
representations are then used along with the same unlabeled human navigation
demonstrations to learn a mapping from the representation space to terrain
costs. At run time, VRL-PAP maps from images to representations and then
representations to costs to perform preference-aware path planning. We present
empirical results from challenging outdoor settings that demonstrate VRL-PAP 1)
is successfully able to pick paths that reflect demonstrated preferences, 2) is
comparable in execution to geometric navigation with a highly detailed manually
annotated map (without requiring such annotations), 3) is able to generalize to
novel terrain types with minimal additional unlabeled demonstrations.

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The categories of this paper are: eess
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The categories of this paper are: solv-int
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
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The categories of this paper are: nlin
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The categories of this paper are: supr-con
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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: bayes-an
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The categories of this paper are: q-bio
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The categories of this paper are: adap-org
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The categories of this paper are: physics