Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Motion-Nets: 6D Tracking of Unknown Objects in Unseen Environments using
  RGB   In this work, we bridge the gap between recent pose estimation and tracking
work to develop a powerful method for robots to track objects in their
surroundings. Motion-Nets use a segmentation model to segment the scene, and
separate translation and rotation models to identify the relative 6D motion of
an object between two consecutive frames. We train our method with generated
data of floating objects, and then test on several prediction tasks, including
one with a real PR2 robot, and a toy control task with a simulated PR2 robot
never seen during training. Motion-Nets are able to track the pose of objects
with some quantitative accuracy for about 30-60 frames including occlusions and
distractors. Additionally, the single step prediction errors remain low even
after 100 frames. We also investigate an iterative correction procedure to
improve performance for control tasks.

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The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
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The categories of this paper are: physics
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an