  Recently, learning-based approaches show promising results in navigation
tasks. However, the poor generalization capability and the simulation-reality
gap prevent a wide range of applications. We consider the problem of improving
the generalization of mobile robots and achieving sim-to-real transfer for
navigation skills. To that end, we propose a cross-modal fusion method and a
knowledge transfer framework for better generalization. This is realized by a
teacher-student distillation architecture. The teacher learns a discriminative
representation and the near-perfect policy in an ideal environment. By
imitating the behavior and representation of the teacher, the student is able
to align the features from noisy multi-modal input and reduce the influence of
variations on navigation policy. We evaluate our method in simulated and
real-world environments. Experiments show that our method outperforms the
baselines by a large margin and achieves robust navigation performance with
varying working conditions.
