Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Compressed Learning for Tactile Object Classification   The potential of large tactile arrays to improve robot perception for safe
operation in human-dominated environments and of high-resolution tactile arrays
to enable human-level dexterous manipulation is well accepted. However, the
increase in the number of tactile sensing elements introduces challenges
including wiring complexity, power consumption, and data processing. To help
address these challenges, we previously developed a tactile sensing technique
based compressed sensing that reduces hardware complexity and data
transmission, while allowing accurate reconstruction of the full-resolution
signal. In this paper, we apply tactile compressed sensing to the problem of
object classification. Specifically, we perform object classification on the
compressed tactile data. We evaluate our method using BubbleTouch, our tactile
array simulator. Our results show our approach achieves high classification
accuracy, even with compression factors up to 64.

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The categories of this paper are: nucl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
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The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: eess
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The categories of this paper are: q-bio
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The categories of this paper are: nlin
30
The categories of this paper are: q-alg
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The categories of this paper are: hep-th
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The categories of this paper are: q-fin