$S^3$-Nav: Structured Spatial-Semantic Decision Making for Language-Guided Robot Navigation
From the abstract
This paper presents $S^3$-Nav, a structured spatial-semantic decision-making framework for language-guided robot navigation under partial observability. Rather than assuming a complete environment representation in advance, $S^3$-Nav incrementally constructs persistent spatial-semantic state while exposing only a capacity-controlled, task-conditioned view to the high-level policy.
From the abstract. Our summary is in progress.
The full text is not republished here because the paper's license does not allow it. Read the original on arXiv.