Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Deep Learning Approach To Multi-Context Socially-Aware Navigation   We present a context classification pipeline to allow a robot to change its
navigation strategy based on the observed social scenario. Socially-Aware
Navigation considers social behavior in order to improve navigation around
people. Most of the existing research uses different techniques to incorporate
social norms into robot path planning for a single context. Methods that work
for hallway behavior might not work for approaching people, and so on. We
developed a high-level decision-making subsystem, a model-based context
classifier, and a multi-objective optimization-based local planner to achieve
socially-aware trajectories for autonomously sensed contexts. Using a context
classification system, the robot can select social objectives that are later
used by Pareto Concavity Elimination Transformation (PaCcET) based local
planner to generate safe, comfortable, and socially-appropriate trajectories
for its environment. This was tested and validated in multiple environments on
a Pioneer mobile robot platform; results show that the robot was able to select
and account for social objectives related to navigation autonomously.

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The categories of this paper are: hep-th
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The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
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The categories of this paper are: stat
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The categories of this paper are: supr-con
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The categories of this paper are: cond-mat
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The categories of this paper are: nlin
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The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org