Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Real-Time, Highly Accurate Robotic Grasp Detection using Fully
  Convolutional Neural Network with Rotation Ensemble Module   Rotation invariance has been an important topic in computer vision tasks.
Ideally, robot grasp detection should be rotation-invariant. However,
rotation-invariance in robotic grasp detection has been only recently studied
by using rotation anchor box that are often time-consuming and unreliable for
multiple objects. In this paper, we propose a rotation ensemble module (REM)
for robotic grasp detection using convolutions that rotates network weights.
Our proposed REM was able to outperform current state-of-the-art methods by
achieving up to 99.2% (image-wise), 98.6% (object-wise) accuracies on the
Cornell dataset with real-time computation (50 frames per second). Our proposed
method was also able to yield reliable grasps for multiple objects and up to
93.8% success rate for the real-time robotic grasping task with a 4-axis robot
arm for small novel objects that was significantly higher than the baseline
methods by 11-56%.

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The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: funct-an
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
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The categories of this paper are: econ
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The categories of this paper are: chem-ph
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The categories of this paper are: ao-sci
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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: physics
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The categories of this paper are: math-ph
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The categories of this paper are: alg-geom