Jia Li, Yinfeng Yu, Liejun Wang et al.
In audio-visual navigation (AVN) tasks, an embodied agent must autonomously localize a sound source in unknown and complex 3D environments based on audio-visual signals. Existing methods often rely on static modality fusion strategies and neglect the spatial cues embedded in stereo audio, leading to performance degradation in cluttered or occluded scenes. To address these issues, we propose an end-to-end reinforcement learning-based AVN framework with two key innovations: (1) a \textbf{S}tereo-Aware \textbf{A}ttention \textbf{M}odule (\textbf{SAM}), which learns and exploits the spatial disparity between left and right audio channels to enhance directional sound perception; and (2) an \textbf{A}udio-\textbf{G}uided \textbf{D}ynamic \textbf{F}usion Module (\textbf{AGDF}), which dynamically adjusts the fusion ratio between visual and auditory features based on audio cues, thereby improving robustness to environmental changes. Extensive experiments are conducted on two realistic 3D scene datasets, Replica and Matterport3D, demonstrating that our method significantly outperforms existing approaches in terms of navigation success rate and path efficiency. Notably, our model achieves over 40\% improvement under audio-only conditions compared to the best-performing baselines. These results highlight the importance of explicitly modeling spatial cues from stereo channels and performing deep multi-modal fusion for robust and efficient audio-visual navigation.