  Reliable navigation systems have a wide range of applications in robotics and
autonomous driving. Current approaches employ an open-loop process that
converts sensor inputs directly into actions. However, these open-loop schemes
are challenging to handle complex and dynamic real-world scenarios due to their
poor generalization. Imitating human navigation, we add a reasoning process to
convert actions back to internal latent states, forming a two-stage closed loop
of perception, decision-making, and reasoning. Firstly, VAE-Enhanced
Demonstration Learning endows the model with the understanding of basic
navigation rules. Then, two dual processes in RL-Enhanced Interaction Learning
generate reward feedback for each other and collectively enhance obstacle
avoidance capability. The reasoning model can substantially promote
generalization and robustness, and facilitate the deployment of the algorithm
to real-world robots without elaborate transfers. Experiments show our method
is more adaptable to novel scenarios compared with state-of-the-art approaches.
