Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: No RL, No Simulation: Learning to Navigate without Navigating   Most prior methods for learning navigation policies require access to
simulation environments, as they need online policy interaction and rely on
ground-truth maps for rewards. However, building simulators is expensive
(requires manual effort for each and every scene) and creates challenges in
transferring learned policies to robotic platforms in the real-world, due to
the sim-to-real domain gap. In this paper, we pose a simple question: Do we
really need active interaction, ground-truth maps or even
reinforcement-learning (RL) in order to solve the image-goal navigation task?
We propose a self-supervised approach to learn to navigate from only passive
videos of roaming. Our approach, No RL, No Simulator (NRNS), is simple and
scalable, yet highly effective. NRNS outperforms RL-based formulations by a
significant margin. We present NRNS as a strong baseline for any future
image-based navigation tasks that use RL or Simulation.

18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph