  Autonomously navigating a robot in everyday crowded spaces requires solving
complex perception and planning challenges. When using only monocular image
sensor data as input, classical two-dimensional planning approaches cannot be
used. While images present a significant challenge when it comes to perception
and planning, they also allow capturing potentially important details, such as
complex geometry, body movement, and other visual cues. In order to
successfully solve the navigation task from only images, algorithms must be
able to model the scene and its dynamics using only this channel of
information. We investigate whether the world model concept, which has shown
state-of-the-art results for modeling and learning policies in Atari games as
well as promising results in 2D LiDAR-based crowd navigation, can also be
applied to the camera-based navigation problem. To this end, we create
simulated environments where a robot must navigate past static and moving
humans without colliding in order to reach its goal. We find that
state-of-the-art methods are able to achieve success in solving the navigation
problem, and can generate dream-like predictions of future image-sequences
which show consistent geometry and moving persons. We are also able to show
that policy performance in our high-fidelity sim2real simulation scenario
transfers to the real world by testing the policy on a real robot. We make our
simulator, models and experiments available at
https://github.com/danieldugas/NavDreams.
