Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: End-to-end grasping policies for human-in-the-loop robots via deep
  reinforcement learning   State-of-the-art human-in-the-loop robot grasping is hugely suffered by
Electromyography (EMG) inference robustness issues. As a workaround,
researchers have been looking into integrating EMG with other signals, often in
an ad hoc manner. In this paper, we are presenting a method for end-to-end
training of a policy for human-in-the-loop robot grasping on real reaching
trajectories. For this purpose we use Reinforcement Learning (RL) and Imitation
Learning (IL) in DEXTRON (DEXTerity enviRONment), a stochastic simulation
environment with real human trajectories that are augmented and selected using
a Monte Carlo (MC) simulation method. We also offer a success model which once
trained on the expert policy data and the RL policy roll-out transitions, can
provide transparency to how the deep policy works and when it is probably going
to fail.

34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
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
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat