Enhancing Zero-Shot Object Navigation via LLM-Inferred Co-occurring Objects

Abstract

How do humans navigate to a target object in an unmapped, unseen environment? We certainly do not wander aimlessly. Instead, human explorers naturally rely on spatial context, leveraging the inherent co-occurrence of everyday objects to infer a target’s probable location. However, conventional zero-shot object navigation methods construct a value map using only the target object, directing the agent toward the frontier with the highest score. This paper proposes a framework that leverages contextual cues from a Large Language Model (LLM). The proposed method uses an LLM to infer co-occurring objects near the target object. Based on current observations, the exploration direction is toward the frontiers where both the target and its co-occurring objects are most likely to be found. To compute the similarity between the target object and co-occurring objects, we designed value map fusion methods using static, dynamic, and adaptive structures. We verified the generalizability of our approach by integrating it into existing value-map based zero-shot object navigation models. Evaluations on the HM3D dataset demonstrated that the proposed method improves the Success Rate and the Success weighted by Path Length relative to the baselines. Therefore, mimicking human reasoning processes with LLM-provided contextual cues, we successfully enhanced zero-shot object navigation performance.