Hi-Nav: Hierarchical Framework for Continuous Vision-Language Navigation via Map Guidance and Waypoint Reasoning

Despite remarkable advancements in Vision-and-LanguageNavigation (VLN), a significant sim-to-real gap remains. Most meth-ods are trained and evaluated in simplified or physics-limited simulators,which ignore the feedback loop between decision making and physical ex-ecution, resulting in unstable navigation performance. To bridge this gap,we propose Hi-Nav, a top-down hierarchical navigation framework thatdecomposes VLN into three controllable levels. The high-level moduleleverages a large language model to split long-horizon instructions intosub-tasks. At the middle level, an Interest Score Occupancy Map (ISOM)integrates instruction-relevant semantics with geometric traversability toperceive the surrounding environment, after which waypoint reasoning isperformed for route refinement. ISOM construction and waypoint reason-ing are jointly modeled within a unified multi-task learning framework,where a Q-Former fuses RGB-D observations and navigation instructionsinto shared query representations for efficient multi-task learning, aug-mented with learnable temporal embeddings to mitigate long-horizon for-getting. Finally, low-level behaviors are executed through ROS, enablingobstacle avoidance. We introduce a novel waypoint reasoning datasetfor comprehensive training and build a ROS/Gazebo VLN benchmarkto evaluate methods in a realistic simulation setting, which significantlyreduces the difficulty of sim-to-real transfer. Extensive experiments con-ducted in both Gazebo and the real world demonstrate strong perfor-mance, achieving up to 80% Success Rate (SR) with 0.65 m NavigationError (NE). Our code will be released at https://hiway-page.github.io/.