Object goal navigation aims to navigate an agent to locations of a given
object category in unseen environments. Classical methods explicitly build maps
of environments and require extensive engineering while lacking semantic
information for object-oriented exploration. On the other hand, end-to-end
learning methods alleviate manual map design and predict actions using implicit
representations. Such methods, however, lack an explicit notion of geometry and
may have limited ability to encode navigation history. In this work, we propose
an implicit spatial map for object goal navigation. Our implicit map is
recursively updated with new observations at each step using a transformer. To
encourage spatial reasoning, we introduce auxiliary tasks and train our model
to reconstruct explicit maps as well as to predict visual features, semantic
labels and actions. Our method significantly outperforms the state of the art
on the challenging MP3D dataset and generalizes well to the HM3D dataset. We
successfully deploy our model on a real robot and achieve encouraging object
goal navigation results in real scenes using only a few real-world
demonstrations. Code, trained models and videos are available at
https://www.di.ens.fr/willow/research/onav_rim/.

我们在这篇论文中提出了一种隐式空间地图的目标导航方法，使用 transformer 递归更新隐式地图，并通过辅助任务训练模型，以重构显式地图、预测视觉特征、语义标签和动作。我们的方法在挑战性的 MP3D 数据集上显著优于最先进方法，并且在 HM3D 数据集上具有良好的泛化能力。我们成功地在真实机器人上部署了模型，并在真实场景中只使用了几个真实世界的演示来实现令人鼓舞的目标导航结果。