Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep
  Reinforcement Learning Approach   Industrial robot manipulators are playing a more significant role in modern
manufacturing industries. Though peg-in-hole assembly is a common industrial
task which has been extensively researched, safely solving complex high
precision assembly in an unstructured environment remains an open problem.
Reinforcement Learning (RL) methods have been proven successful in solving
manipulation tasks autonomously. However, RL is still not widely adopted on
real robotic systems because working with real hardware entails additional
challenges, especially when using position-controlled manipulators. The main
contribution of this work is a learning-based method to solve peg-in-hole tasks
with position uncertainty of the hole. We proposed the use of an off-policy
model-free reinforcement learning method and bootstrap the training speed by
using several transfer learning techniques (sim2real) and domain randomization.
Our proposed learning framework for position-controlled robots was extensively
evaluated on contact-rich insertion tasks on a variety of environments.

15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci