2026
QuASH: Using Natural-Language Heuristics to Query Visual-Language Robotic Maps
ICRA 2026poster
Embeddings from Visual-Language Models are increasingly utilized to represent semantics in robotic maps, offering an open-vocabulary scene understanding that surpasses traditional, limited labels. Embeddings enable on-demand querying by comparing embedded user text prompts to map embeddings via a si…