Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Demonstration-Guided Deep Reinforcement Learning of Control Policies for
  Dexterous Human-Robot Interaction   In this paper, we propose a method for training control policies for
human-robot interactions such as handshakes or hand claps via Deep
Reinforcement Learning. The policy controls a humanoid Shadow Dexterous Hand,
attached to a robot arm. We propose a parameterizable multi-objective reward
function that allows learning of a variety of interactions without changing the
reward structure. The parameters of the reward function are estimated directly
from motion capture data of human-human interactions in order to produce
policies that are perceived as being natural and human-like by observers. We
evaluate our method on three significantly different hand interactions:
handshake, hand clap and finger touch. We provide detailed analysis of the
proposed reward function and the resulting policies and conduct a large-scale
user study, indicating that our policy produces natural looking motions.

2
The categories of this paper are: math
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc