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Related Experiment Video

Updated: Aug 2, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Regularized Denoising Masked Visual Pretraining for Robust Embodied PointGoal Navigation.

Jie Peng1,2, Yangbin Xu1,2, Luqing Luo1

  • 1Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary

This study introduces a new navigation framework, Regularized Denoising Masked AutoEncoders Navigation (RDMAE-Nav), to enhance embodied agent robustness against visual corruptions like noise and blur. RDMAE-Nav significantly improves navigation performance in challenging visual conditions.

Keywords:
Kullback–Leibler divergencedenoisingembodied AImasked visual pretrainingrobust visual navigationself-supervised learningvision transformer

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computer Vision
  • Machine Learning

Background:

  • Embodied PointGoal navigation is crucial for autonomous agents but is highly sensitive to visual corruptions.
  • Existing embodied navigation agents exhibit poor robustness against visual degradations such as Spatter, Speckle Noise, and Defocus Blur.
  • There is a need for robust visual representation learning to improve agent performance in real-world, visually challenging environments.

Purpose of the Study:

  • To propose a novel navigation framework, RDMAE-Nav, designed to enhance the robustness of embodied agents against various visual corruptions.
  • To introduce a self-supervised pretraining method, Regularized Denoising Masked AutoEncoders (RDMAE), for learning robust visual representations.
  • To improve the performance of embodied navigation agents in the presence of visual noise and blur.

Main Methods:

  • Developed RDMAE-Nav, a framework integrating a visual module with a policy module for embodied navigation.
  • Introduced RDMAE, a self-supervised pretraining method using denoising masked autoencoding and bidirectional Kullback-Leibler divergence for Vision Transformers (ViT).
  • Applied RDMAE to pretrain a ViT-based visual encoder, which was then used with frozen weights for policy learning in the navigation task.

Main Results:

  • RDMAE-Nav demonstrated competitive performance against state-of-the-art methods across various visual corruptions.
  • Significant absolute improvements were observed: 28.2% SR and 23.68% SPL under Spatter.
  • Further improvements included 2.28% SR and 6.41% SPL under Speckle Noise, and 9.46% SR and 9.55% SPL under Defocus Blur.

Conclusions:

  • RDMAE-Nav effectively enhances the robustness of embodied navigation agents to visual corruptions.
  • The proposed RDMAE pretraining method enables learning of strong visual representations for navigation tasks.
  • RDMAE-Nav offers a promising solution for deploying embodied agents in visually degraded environments.