Review for NeurIPS paper: Object Goal Navigation using Goal-Oriented Semantic Exploration

Summary and Contributions: This paper presents an extension to recent work on Active Neural SLAM [1], where semantic information about object categories is explicitly incorporated into the model. The extensions in the model architecture provide explicit semantic information about the various objects of the scene in the generated 2D map, that allows an agent to navigate in its environment and find a specified goal object much efficiently compared to baselines. Some of these baselines use - and others do not - semantic information. The comparison was performed using Gibson [2] and Matterport3D (MP3D) [3], which include 3D reconstructions of real environments. Training was performed on 86 scenes and testing on 16.