$S^3$-Nav: Structured Spatial-Semantic Decision Making for Language-Guided Robot Navigation

This research introduces $S^3$-Nav, a framework combining online mapping, persistent object-relation memory, and goal-conditioned navigation for robots operating under partial observability. Using RGB-D inputs and SLAM, the system maintains a structured state of object records and confirmed relations without needing a pre-existing map. A goal-conditioned evidence interface selects relevant information to guide high-level decisions on exploration, approach, and stopping. The framework validates provisional policy decisions against persistent memory and geometric evidence before execution. Evaluations across multiple environments demonstrate improved closed-loop navigation reliability and reduced language-model token consumption when using capacity-controlled evidence exposure.