Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Deep Learning Based Behavioral Approach to Indoor Autonomous
  Navigation   We present a semantically rich graph representation for indoor robotic
navigation. Our graph representation encodes: semantic locations such as
offices or corridors as nodes, and navigational behaviors such as enter office
or cross a corridor as edges. In particular, our navigational behaviors operate
directly from visual inputs to produce motor controls and are implemented with
deep learning architectures. This enables the robot to avoid explicit
computation of its precise location or the geometry of the environment, and
enables navigation at a higher level of semantic abstraction. We evaluate the
effectiveness of our representation by simulating navigation tasks in a large
number of virtual environments. Our results show that using a simple sets of
perceptual and navigational behaviors, the proposed approach can successfully
guide the way of the robot as it completes navigational missions such as going
to a specific office. Furthermore, our implementation shows to be effective to
control the selection and switching of behaviors.

31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org