STRIVE: Structured Representation Integrating VLM Reasoning for Efficient Object Navigation

Figure 1: STRIVE can conduct zero-shot object navigation in diverse and complex real-world environments by leveraging our novel multi-layer representation and efficient two-stage navigation policy. Abstract-- Vision-Language Models (VLMs) have been increasingly integrated into object navigation tasks for their rich prior knowledge and strong reasoning abilities. However, applying VLMs to navigation presents two key challenges: effectively parsing and structuring complex environment information and determining when and how to query VLMs. T o address these challenges, we propose a novel framework that incrementally constructs a multi-layer environment representation consisting of viewpoints, object nodes, and room nodes during navigation. Viewpoints and object nodes facilitate intra-room exploration and accurate target localization, while room nodes support efficient inter-room planning. Building on this structured representation, we propose a novel two-stage navigation policy, integrating high-level planning guided by VLM reasoning with low-level VLM-assisted exploration to efficiently and reliably locate a goal object. Object navigation is a fundamental task in robotics, where an agent must locate an instance of a given object category in unknown environments. This task is particularly challenging, as it requires the agent to understand complex visual information, reason about spatial relationships, and make decisions based on both current and past observations. Advances in Vision-Language Models (VLMs) [1], [2], [3] have demonstrated strong capabilities in contextual visual understanding and common-sense reasoning. However, existing approaches often face two significant challenges: First, the input to VLMs typically lacks a structured representation of the environment and is often restricted to local observations.