Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Navigational Instruction Generation as Inverse Reinforcement Learning
  with Neural Machine Translation   Modern robotics applications that involve human-robot interaction require
robots to be able to communicate with humans seamlessly and effectively.
Natural language provides a flexible and efficient medium through which robots
can exchange information with their human partners. Significant advancements
have been made in developing robots capable of interpreting free-form
instructions, but less attention has been devoted to endowing robots with the
ability to generate natural language. We propose a navigational guide model
that enables robots to generate natural language instructions that allow humans
to navigate a priori unknown environments. We first decide which information to
share with the user according to their preferences, using a policy trained from
human demonstrations via inverse reinforcement learning. We then "translate"
this information into a natural language instruction using a neural
sequence-to-sequence model that learns to generate free-form instructions from
natural language corpora. We evaluate our method on a benchmark route
instruction dataset and achieve a BLEU score of 72.18% when compared to
human-generated reference instructions. We additionally conduct navigation
experiments with human participants that demonstrate that our method generates
instructions that people follow as accurately and easily as those produced by
humans.

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The categories of this paper are: math
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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: econ
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The categories of this paper are: adap-org
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-bio
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The categories of this paper are: ao-sci
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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
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The categories of this paper are: eess