Robust Navigation with Cross-Modal Fusion and Knowledge Transfer

• URL: http://arxiv.org/abs/2309.13266v1
• Date: Sat, 23 Sep 2023 05:16:35 GMT
• Title: Robust Navigation with Cross-Modal Fusion and Knowledge Transfer
• Authors: Wenzhe Cai, Guangran Cheng, Lingyue Kong, Lu Dong, Changyin Sun
• Abstract summary: We consider the problem of improving the generalization of mobile robots. We propose a cross-modal fusion method and a knowledge transfer framework for better generalization. By imitating the behavior and representation of the teacher, the student is able to align the features from noisy multi-modal input.
• Score: 16.529923581195753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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