Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning for Microrobot Exploration: Model-based Locomotion,
  Sparse-robust Navigation, and Low-power Deep Classification   Building intelligent autonomous systems at any scale is challenging. The
sensing and computation constraints of a microrobot platform make the problems
harder. We present improvements to learning-based methods for on-board learning
of locomotion, classification, and navigation of microrobots. We show how
simulated locomotion can be achieved with model-based reinforcement learning
via on-board sensor data distilled into control. Next, we introduce a sparse,
linear detector and a Dynamic Thresholding method to FAST Visual Odometry for
improved navigation in the noisy regime of mm scale imagery. We end with a new
image classifier capable of classification with fewer than one million
multiply-and-accumulate (MAC) operations by combining fast downsampling,
efficient layer structures and hard activation functions. These are promising
steps toward using state-of-the-art algorithms in the power-limited world of
edge-intelligence and microrobots.

13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
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The categories of this paper are: econ
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph