REGNav: Room Expert Guided Image-Goal Navigation

TL;DR

REGNav introduces a novel approach for image-goal navigation by incorporating a room expert that assesses whether the current observation and goal images are from the same room, improving navigation accuracy.

Contribution

The paper proposes REGNav, a new model that uses an unsupervised pre-trained room expert to analyze room similarity, enhancing navigation performance over prior methods.

Findings

Outperforms state-of-the-art on three benchmarks.

Effectively distinguishes same-room and different-room scenarios.

Improves navigation success rate significantly.

Abstract

Image-goal navigation aims to steer an agent towards the goal location specified by an image. Most prior methods tackle this task by learning a navigation policy, which extracts visual features of goal and observation images, compares their similarity and predicts actions. However, if the agent is in a different room from the goal image, it's extremely challenging to identify their similarity and infer the likely goal location, which may result in the agent wandering around. Intuitively, when humans carry out this task, they may roughly compare the current observation with the goal image, having an approximate concept of whether they are in the same room before executing the actions. Inspired by this intuition, we try to imitate human behaviour and propose a Room Expert Guided Image-Goal Navigation model (REGNav) to equip the agent with the ability to analyze whether goal and observation images are taken in the same room. Specifically, we first pre-train a room expert with an unsupervised learning technique on the self-collected unlabelled room images. The expert can extract the hidden room style information of goal and observation images and predict their relationship about whether they belong to the same room. In addition, two different fusion approaches are explored to efficiently guide the agent navigation with the room relation knowledge. Extensive experiments show that our REGNav surpasses prior state-of-the-art works on three popular benchmarks.