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

This paper proposes to train an ObjectNav policy that generalises to unseen environments by using a modular system that classifies objects and builds an episodic semantic map, which it is uses to explore the environment based on the object category, building upon the hierarchical method in "Learning to explore using Active Neural SLAM". The method achieved SOTA performance on the 2020 CVPR Object Goal Navigation Habitat Challenge. Interestingly, the policy, trained on Gibson and MP3D, has been transferred and deployed in a real robot, with some success. While the initial reviews were mixed (9, 7, 4, 5), the reviewers converged on (8, 7, 6, 6), agreeing during discussion that the paper deserved to be accepted. Based on the reviews, I recommend this paper for acceptance as a spotlight or poster presentation.