SOURCE-LINKED INTELLIGENCE

SparseNav: Instruction-conditioned Sparse Semantic Perception for Training-Free Vision-Language Navigation

arXiv · AI, language, vision and robotics · article · Sep 22, 2026 · UTC

Map-based vision-language navigation (VLN) relies on persistent spatial representations to connect language understanding with geometric planning. However, acquiring semantics beyond the needs of the current instruction can introduce unnecessary perception cost and irrelevant annotations. Continuously accumulating unrelated objects may not only waste computation, but also clutter the visual-spatial representation consumed by the vision-language model (VLM) planner. To address this problem, we present SparseNav, a training-free framework that follows a less-is-more principle for semantic naviga

Read original source ↗ Open in workspace

recordType

paper

region

Global