Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Viewpoint Selection for Grasp Detection   In grasp detection, the robot estimates the position and orientation of
potential grasp configurations directly from sensor data. This paper explores
the relationship between viewpoint and grasp detection performance.
Specifically, we consider the scenario where the approximate position and
orientation of a desired grasp is known in advance and we want to select a
viewpoint that will enable a grasp detection algorithm to localize it more
precisely and with higher confidence. Our main findings are that the right
viewpoint can dramatically increase the number of detected grasps and the
classification accuracy of the top-n detections. We use this insight to create
a viewpoint selection algorithm and compare it against a random viewpoint
selection strategy and a strategy that views the desired grasp head-on. We find
that the head-on strategy and our proposed viewpoint selection strategy can
improve grasp success rates on a real robot by 8% and 4%, respectively.
Moreover, we find that the combination of the two methods can improve grasp
success rates by as much as 12%.

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The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
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The categories of this paper are: funct-an
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc