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.

翻译：近年来，基于学习的方法在导航任务中展现出良好前景。然而，泛化能力不足以及仿真与现实的差距限制了其广泛应用。本文研究如何提升移动机器人的泛化能力并实现导航技能的仿真到现实迁移。为此，我们提出了一种跨模态融合方法与知识迁移框架以增强泛化性能。该方案通过师生蒸馏架构实现：教师网络在理想环境中学习判别性表征与近似最优策略，学生网络通过模仿教师的行为与表征，能够对齐来自含噪多模态输入的特征，并减少环境变化对导航策略的影响。我们在仿真与真实环境中评估了该方法，实验表明，本方法大幅超越基线模型，并在不同工作条件下实现了鲁棒的导航性能。