STRIVE: Structured Representation Integrating VLM Reasoning for Efficient Object Navigation

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. Insufficient environment understanding and over-reliance on VLMs (e.g. querying at every step) can easily lead to unnecessary backtracking and reduced navigation efficiency, especially in large continuous environments. To 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. We evaluated our approach on four simulated benchmarks (HM3D v1&v2, RoboTHOR, and MP3D), and achieved state-of-the-art performance on both the success rate (SR% 13.1%) and navigation efficiency (SPL% 6.2%). We further validate our method on a real robot platform, demonstrating strong robustness across 120 episodes in 10 different indoor environments. Project page is available at: https://zwandering.github.io/STRIVE.github.io/.