  Sociability is essential for modern robots to increase their acceptability in
human environments. Traditional techniques use manually engineered utility
functions inspired by observing pedestrian behaviors to achieve social
navigation. However, social aspects of navigation are diverse, changing across
different types of environments, societies, and population densities, making it
unrealistic to use hand-crafted techniques in each domain. This paper presents
a data-driven navigation architecture that uses state-of-the-art neural
architectures, namely Conditional Neural Processes, to learn global and local
controllers of the mobile robot from observations. Additionally, we leverage a
state-of-the-art, deep prediction mechanism to detect situations not similar to
the trained ones, where reactive controllers step in to ensure safe navigation.
Our results demonstrate that the proposed framework can successfully carry out
navigation tasks regarding social norms in the data. Further, we showed that
our system produces fewer personal-zone violations, causing less discomfort.
