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Embodied PointGoal navigation is a fundamental task for embodied agents. Recent works have shown that the performance of the embodied navigation agent degrades significantly in the presence of visual corruption, including Spatter, Speckle Noise, and Defocus Blur, showing the weak robustness of the agent. To improve the robustness of embodied navigation agents to various visual corruptions, we propose a navigation framework called Regularized Denoising Masked AutoEncoders Navigation (RDMAE-Nav). In a nutshell, RDMAE-Nav mainly consists of two modules: a visual module and a policy module. In the visual module, a self-supervised pretraining method, dubbed Regularized Denoising Masked AutoEncoders (RDMAE), is designed to enable the Vision Transformers (ViT)-based visual encoder to learn robust representations. The bidirectional Kullback–Leibler divergence is introduced in RDMAE as the regularization term for a denoising masked modeling task. Specifically, RDMAE mitigates the gap between clean and noisy image representations by minimizing the bidirectional Kullback–Leibler divergence. Then, the visual encoder is pretrained by RDMAE. In contrast to existing works, RDMAE-Nav applies denoising masked visual pretraining for PointGoal navigation to improve robustness to various visual corruptions. Finally, the pretrained visual encoder with frozen weights is applied to extract robust visual representations for policy learning in the RDMAE-Nav. Extensive experiments show that RDMAE-Nav performs competitively compared with state of the arts (SOTAs) on various visual corruptions. In detail, RDMAE-Nav performs the absolute improvement: 28.2% in SR and 23.68% in SPL under Spatter; 2.28% in SR and 6.41% in SPL under Speckle Noise; and 9.46% in SR and 9.55% in SPL under Defocus Blur.

In this work, the thermo-oxidative aging performance of antioxidant N-isopropyl-N′-phenyl-p-phenylenediamine (4010NA)/silica (SiO2)/natural rubber (NR) composite was evaluated by the variations of mechanical properties and chemical structure after aging at 100 °C. Compared with 4010NA/carbon black (CB)/NR composite, there existed significant synergistic effects between 4010NA and SiO2 on improving the thermo-oxidative stability of NR material. Then, by combining the results of experiments and molecular simulations, the synergistic stabilization mechanism in 4010NA/SiO2/NR composite could be attributed to the following comprehensive effects. Firstly, the incorporation of silica was capable of improving oxygen (O2) barrier ability of NR and inhibiting the migration of 4010NA. Secondly, antioxidant 4010NA could be prior to terminate peroxy radicals, and its addition enabled 4010NA/SiO2/NR composite to be of an appropriately high cross-linking density due to a vulcanization accelerated effect. As a result, 4010NA/SiO2/NR composite exhibited more preeminent mechanical properties than 4010NA/NR and 4010NA/CB/NR composites during the thermo-oxidative aging process. This means that the combination of 4010NA and SiO2 can be exploited to prepare high performance NR composite with a facile operation. Furthermore, molecular simulation, as a theoretical method, may contribute to our understanding about structure-properties relationship for NR composite in depth. Keywords: Natural rubber (NR) aging, Silica, Antioxidant, Synergistic effect, Molecular simulation