Navigating the Wild: Pareto-Optimal Visual Decision-Making in Image Space

Humans possess a remarkable ability to navigate complex environments by intuitively interpreting visual scenes at a semantic level - effortlessly distinguishing between walkable paths, obstacles, and hazardous areas while adapting to diverse terrain conditions (Dwivedi et al. 2024). This natural ability to understand both the semantic meaning and traversability of environmental elements has inspired the development of visual semantic navigation systems for autonomous robots. Through semantic segmentation of the environment, robots can identify traversable spaces and obstacles, moving closer to achieving human-like navigation capabilities in challenging real-world applications. A motivating scenario is shown in Figure 1. Visual semantic navigation is especially crucial in field robotics applications.