Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Federated Imitation Learning: A Privacy Considered Imitation Learning
  Framework for Cloud Robotic Systems with Heterogeneous Sensor Data   Humans are capable of learning a new behavior by observing others perform the
skill. Robots can also implement this by imitation learning. Furthermore, if
with external guidance, humans will master the new behavior more efficiently.
So how can robots implement this? To address the issue, we present Federated
Imitation Learning (FIL) in the paper. Firstly, a knowledge fusion algorithm
deployed on the cloud for fusing knowledge from local robots is presented.
Then, effective transfer learning methods in FIL are introduced. With FIL, a
robot is capable of utilizing knowledge from other robots to increase its
imitation learning. FIL considers information privacy and data heterogeneity
when robots share knowledge. It is suitable to be deployed in cloud robotic
systems. Finally, we conduct experiments of a simplified self-driving task for
robots (cars). The experimental results demonstrate that FIL is capable of
increasing imitation learning of local robots in cloud robotic systems.

36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex