  Image-goal navigation is a challenging task, as it requires the agent to
navigate to a target indicated by an image in a previously unseen scene.
Current methods introduce diverse memory mechanisms which save navigation
history to solve this task. However, these methods use all observations in the
memory for generating navigation actions without considering which fraction of
this memory is informative. To address this limitation, we present the MemoNav,
a novel memory mechanism for image-goal navigation, which retains the agent's
informative short-term memory and long-term memory to improve the navigation
performance on a multi-goal task. The node features on the agent's topological
map are stored in the short-term memory, as these features are dynamically
updated. To aid the short-term memory, we also generate long-term memory by
continuously aggregating the short-term memory via a graph attention module.
The MemoNav retains the informative fraction of the short-term memory via a
forgetting module based on a Transformer decoder and then incorporates this
retained short-term memory and the long-term memory into working memory.
Lastly, the agent uses the working memory for action generation. We evaluate
our model on a new multi-goal navigation dataset. The experimental results show
that the MemoNav outperforms the SoTA methods by a large margin with a smaller
fraction of navigation history. The results also empirically show that our
model is less likely to be trapped in a deadlock, which further validates that
the MemoNav improves the agent's navigation efficiency by reducing redundant
steps.
