TTA-Nav: Test-time Adaptive Reconstruction for Point-Goal Navigation under Visual Corruptions
<|reference_start|>TTA-Nav: Test-time Adaptive Reconstruction for Point-Goal Navigation under Visual Corruptions: 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. Project page: https://sites.google.com/view/tta-nav<|reference_end|>
@article{piriyajitakonkij2024tta-nav:,
    title={TTA-Nav: Test-time Adaptive Reconstruction for Point-Goal Navigation
  under Visual Corruptions},
    author={Maytus Piriyajitakonkij, Mingfei Sun, Mengmi Zhang, Wei Pan},
    journal={arXiv preprint arXiv:2403.01977},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01977},
    primaryClass={cs.RO cs.AI cs.CV}
}