  Traditional path-planning techniques treat humans as obstacles. This has
changed since robots started to enter human environments. On modern robots,
social navigation has become an important aspect of navigation systems. To use
learning-based techniques to achieve social navigation, a powerful framework
that is capable of representing complex functions with as few data as possible
is required. In this study, we benefited from recent advances in deep learning
at both global and local planning levels to achieve human-aware navigation on a
simulated robot. Two distinct deep models are trained with respective
objectives: one for global planning and one for local planning. These models
are then employed in the simulated robot. In the end, it has been shown that
our model can successfully carry out both global and local planning tasks. We
have shown that our system could generate paths that successfully reach targets
while avoiding obstacles with better performance compared to feed-forward
neural networks.
