Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Domain Randomization for Active Pose Estimation   Accurate state estimation is a fundamental component of robotic control. In
robotic manipulation tasks, as is our focus in this work, state estimation is
essential for identifying the positions of objects in the scene, forming the
basis of the manipulation plan. However, pose estimation typically requires
expensive 3D cameras or additional instrumentation such as fiducial markers to
perform accurately. Recently, Tobin et al.~introduced an approach to pose
estimation based on domain randomization, where a neural network is trained to
predict pose directly from a 2D image of the scene. The network is trained on
computer-generated images with a high variation in textures and lighting,
thereby generalizing to real-world images. In this work, we investigate how to
improve the accuracy of domain randomization based pose estimation. Our main
idea is that active perception -- moving the robot to get a better estimate of
pose -- can be trained in simulation and transferred to real using domain
randomization. In our approach, the robot trains in a domain-randomized
simulation how to estimate pose from a \emph{sequence} of images. We show that
our approach can significantly improve the accuracy of standard pose estimation
in several scenarios: when the robot holding an object moves, when reference
objects are moved in the scene, or when the camera is moved around the object.

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The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
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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: stat
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The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: q-bio
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The categories of this paper are: alg-geom
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The categories of this paper are: ao-sci
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The categories of this paper are: acc-phys