Efficient ObjectGoal navigation (ObjectNav) in novel environments requires an
understanding of the spatial and semantic regularities in environment layouts.
In this work, we present a straightforward method for learning these
regularities by predicting the locations of unobserved objects from incomplete
semantic maps. Our method differs from previous prediction-based navigation
methods, such as frontier potential prediction or egocentric map completion, by
directly predicting unseen targets while leveraging the global context from all
previously explored areas. Our prediction model is lightweight and can be
trained in a supervised manner using a relatively small amount of passively
collected data. Once trained, the model can be incorporated into a modular
pipeline for ObjectNav without the need for any reinforcement learning. We
validate the effectiveness of our method on the HM3D and MP3D ObjectNav
datasets. We find that it achieves the state-of-the-art on both datasets,
despite not using any additional data for training.

本文提出了一种学习环境中空间和语义规律的简单方法，通过从不完整的语义地图中预测未观测对象的位置来实现。该方法可以轻松地使用少量数据进行监督式训练，并可以在没有强化学习的情况下，将模型融入到 ObjectNav 的模块化流程中，该模型在 HM3D 和 MP3D ObjectNav 数据集上取得了最佳成果。