Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for
  Robotic Bin-picking   In this paper, we propose an iterative self-training framework for
sim-to-real 6D object pose estimation to facilitate cost-effective robotic
grasping. Given a bin-picking scenario, we establish a photo-realistic
simulator to synthesize abundant virtual data, and use this to train an initial
pose estimation network. This network then takes the role of a teacher model,
which generates pose predictions for unlabeled real data. With these
predictions, we further design a comprehensive adaptive selection scheme to
distinguish reliable results, and leverage them as pseudo labels to update a
student model for pose estimation on real data. To continuously improve the
quality of pseudo labels, we iterate the above steps by taking the trained
student model as a new teacher and re-label real data using the refined teacher
model. We evaluate our method on a public benchmark and our newly-released
dataset, achieving an ADD(-S) improvement of 11.49% and 22.62% respectively.
Our method is also able to improve robotic bin-picking success by 19.54%,
demonstrating the potential of iterative sim-to-real solutions for robotic
applications.

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The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
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The categories of this paper are: q-bio
3
The categories of this paper are: physics