Image-goal navigation is a challenging task that requires an agent to
navigate to a goal indicated by an image in unfamiliar environments. Existing
methods utilizing diverse scene memories suffer from inefficient exploration
since they use all historical observations for decision-making without
considering the goal-relevant fraction. To address this limitation, we present
MemoNav, a novel memory model for image-goal navigation, which utilizes a
working memory-inspired pipeline to improve navigation performance.
Specifically, we employ three types of navigation memory. The node features on
a map are stored in the short-term memory (STM), as these features are
dynamically updated. A forgetting module then retains the informative STM
fraction to increase efficiency. We also introduce long-term memory (LTM) to
learn global scene representations by progressively aggregating STM features.
Subsequently, a graph attention module encodes the retained STM and the LTM to
generate working memory (WM) which contains the scene features essential for
efficient navigation. The synergy among these three memory types boosts
navigation performance by enabling the agent to learn and leverage
goal-relevant scene features within a topological map. Our evaluation on
multi-goal tasks demonstrates that MemoNav significantly outperforms previous
methods across all difficulty levels in both Gibson and Matterport3D scenes.
Qualitative results further illustrate that MemoNav plans more efficient
routes.

MemoNav 是一种新型的内存模型，利用工作内存类似的流水线来提高图像目标导航的性能，通过三种类型的导航内存以及场景特征的学习和利用，使得代理能够学习并利用与目标相关的场景特征在拓扑图中进行高效导航，实验证明 MemoNav 在吉布森和 Matterport3D 场景的多目标任务中显著优于先前的方法，定性结果进一步说明 MemoNav 规划更高效的路径。