Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning a Representation Map for Robot Navigation using Deep
  Variational Autoencoder   The aim of this work is to use Variational Autoencoder (VAE) to learn a
representation of an indoor environment that can be used for robot navigation.
We use images extracted from a video, in which a camera takes a tour around a
house, for training the VAE model with a 4 dimensional latent space. After the
model is trained, each real frame has a corresponding representation point on
manifold in the latent space, and each representation point has corresponding
reconstructed image. For the navigation problem, we map the starting image and
destination image to the latent space, then optimize a path on the learned
manifold connecting the two points, and finally map the path back through
decoder to a sequence of images. The ideal sequence of images should correspond
to a route that is spatially continuous - i.e. neighbor images in the route
should correspond to neighbor locations in physical space. Such a route could
be used for navigation with computer vision techniques, i.e. a robot could
follow the image sequence from starting location to destination in the
environment step by step. We implement this algorithm, but find in our
experimental results that the resulting route is not satisfactory. The route
consist of several discontinuous image frames along the ideal routes, so that
the route could not be followed by a robot with computer vision techniques in
practice. In our evaluation, we propose two reasons for our failure to
automatically find continuous routes: (1) The VAE tends to capture global
structures, but discard the details; (2) the Euclidean similarity metric used
for measuring continuity between house images is sub-optimal. For further work,
we propose: trying other generative models like VAE-GANs which may be better at
reconstructing the details to learn the representation map, and adjusting the
similarity metric in the path selecting algorithm.

30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol