Embodied Visual Navigation

Embodied visual navigation focuses on enabling agents, typically robots, to navigate environments using visual input and instructions. Current research emphasizes improving robustness against adversarial attacks, developing more efficient exploration strategies (like those incorporating memory and novelty-seeking), and creating generalist models capable of handling diverse navigation tasks using large language models (LLMs) and neural potential fields. These advancements are significant for advancing AI capabilities in robotics and have implications for applications ranging from autonomous vehicles to assistive technologies.