Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Robot-Centric Learning from Demonstration via Personalized
  Embeddings   Learning from demonstration (LfD) techniques seek to enable novice users to
teach robots novel tasks in the real world. However, prior work has shown that
robot-centric LfD approaches, such as Dataset Aggregation (DAgger), do not
perform well with human teachers. DAgger requires a human demonstrator to
provide corrective feedback to the learner either in real-time, which can
result in degraded performance due to suboptimal human labels, or in a post hoc
manner which is time intensive and often not feasible. To address this problem,
we present Mutual Information-driven Meta-learning from Demonstration (MIND
MELD), which meta-learns a mapping from poor quality human labels to predicted
ground truth labels, thereby improving upon the performance of prior LfD
approaches for DAgger-based training. The key to our approach for improving
upon suboptimal feedback is mutual information maximization via variational
inference. Our approach learns a meaningful, personalized embedding via
variational inference which informs the mapping from human provided labels to
predicted ground truth labels. We demonstrate our framework in a synthetic
domain and in a human-subjects experiment, illustrating that our approach
improves upon the corrective labels provided by a human demonstrator by 63%.

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The categories of this paper are: nlin
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: q-fin
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The categories of this paper are: eess
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The categories of this paper are: quant-ph
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The categories of this paper are: math-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
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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: solv-int
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: alg-geom