ZeST: an LLM-based Zero-Shot Traversability Navigation for Unknown Environments

--The advancement of robotics and autonomous navigation systems hinges on the ability to accurately predict terrain traversability. Traditional methods for generating datasets to train these prediction models often involve putting robots into potentially hazardous environments, posing risks to equipment and safety. T o solve this problem, we present ZeST, a novel approach leveraging visual reasoning capabilities of Large Language Models (LLMs) to create a traversability map in real-time without exposing robots to danger . Our approach not only performs zero-shot traversability and mitigates the risks associated with real-world data collection but also accelerates the development of advanced navigation systems, offering a cost-effective and scalable solution. T o support our findings, we present navigation results, in both controlled indoor and unstructured outdoor environments. As shown in the experiments, our method provides safer navigation when compared to other state-of-the-art methods, constantly reaching the final goal. The development of autonomous navigation systems is a cornerstone of robotics, with terrain traversability prediction being a critical component [1], [2], [3], [4], [5]. Traversability prediction refers to the ability of a robot to assess whether a given terrain is passable or poses risks to its operation.