As an attempt towards assessing the robustness of embodied navigation agents,
we propose RobustNav, a framework to quantify the performance of embodied
navigation agents when exposed to a wide variety of visual - affecting RGB
inputs - and dynamics - affecting transition dynamics - corruptions. Most
recent efforts in visual navigation have typically focused on generalizing to
novel target environments with similar appearance and dynamics characteristics.
With RobustNav, we find that some standard embodied navigation agents
significantly underperform (or fail) in the presence of visual or dynamics
corruptions. We systematically analyze the kind of idiosyncrasies that emerge
in the behavior of such agents when operating under corruptions. Finally, for
visual corruptions in RobustNav, we show that while standard techniques to
improve robustness such as data-augmentation and self-supervised adaptation
offer some zero-shot resistance and improvements in navigation performance,
there is still a long way to go in terms of recovering lost performance
relative to clean "non-corrupt" settings, warranting more research in this
direction. Our code is available at this https URL

提出 RobustNav 框架，旨在评估具有鲁棒性的导航代理的性能，并在受到各种视觉和动态的影响下量化性能。通过系统分析在不同污染下使用标准导航代理产生的怪异行为，我们发现其中一些标准代理的表现明显下降。对于 RobustNav 中的视觉污染，虽然这些标准技术可以在零次抗阻和导航性能方面提供一些效果，但相对于干净的 “无污染” 设置还有很长的路要走，仍需要更多的研究。