Robot navigation under visual corruption presents a formidable challenge. To address this, we propose a Test-time Adaptation (TTA) method, named as TTA-Nav, for point-goal navigation under visual corruptions. Our "plug-and-play" method incorporates a top-down decoder to a pre-trained navigation model. Firstly, the pre-trained navigation model gets a corrupted image and extracts features. Secondly, the top-down decoder produces the reconstruction given the high-level features extracted by the pre-trained model. Then, it feeds the reconstruction of a corrupted image back to the pre-trained model. Finally, the pre-trained model does forward pass again to output action. Despite being trained solely on clean images, the top-down decoder can reconstruct cleaner images from corrupted ones without the need for gradient-based adaptation. The pre-trained navigation model with our top-down decoder significantly enhances navigation performance across almost all visual corruptions in our benchmarks. Our method improves the success rate of point-goal navigation from the state-of-the-art result of 46% to 94% on the most severe corruption. This suggests its potential for broader application in robotic visual navigation.

通过测试时适应(TTA)方法，我们的研究提出了一种名为TTA-Nav的机器人视觉导航方法，在视觉失真条件下进行目标导向的导航。我们的方法通过将自顶向下解码器与预训练导航模型结合，从失真图像中提取特征，重建较干净的图像，并使导航模型能够在各种视觉失真环境中显著提高导航性能。该方法将目标导向的导航成功率从最先进结果的46%提高到94%，表明其在机器人视觉导航领域具有广泛应用的潜力。

TTA-Nav: 测试时自适应恢复点目标导航在视觉损坏下