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Recently, learning-based approaches show promising results in navigationtasks. However, the poor generalization capability and the simulation-realitygap prevent a wide range of applications. We consider the problem of improvingthe generalization of mobile robots and achieving sim-to-real transfer fornavigation skills. To that end, we propose a cross-modal fusion method and aknowledge transfer framework for better generalization. This is realized by ateacher-student distillation architecture. The teacher learns a discriminativerepresentation and the near-perfect policy in an ideal environment. Byimitating the behavior and representation of the teacher, the student is ableto align the features from noisy multi-modal input and reduce the influence ofvariations on navigation policy. We evaluate our method in simulated andreal-world environments. Experiments show that our method outperforms thebaselines by a large margin and achieves robust navigation performance withvarying working conditions.

Robust Navigation with Cross-Modal Fusion and Knowledge Transfer