Learning Language-Conditioned Traversability Representations for Adaptive Visual Navigation
LaTraNav introduces a dual-system architecture combining a slow Vision-Language Model (VLM) with a fast flow-matching planner. It learns an agent- and language-conditioned traversability representation to bridge the gap between semantic understanding and image path generation. This allows for asynchronous planning, significantly improving path update rates compared to methods relying on explicit masks.