Vision-Language Navigation

Vision-Language Navigation (VLN) asks embodied agents—such as mobile robots and drones—to follow language instructions by interpreting what they see and choosing actions over time. Approaches range from end-to-end vision-language policies to hierarchical systems that combine large models with maps, scene graphs, spatial memory, geometric planners, and world models; imitation learning and reinforcement learning are common training strategies. Methods are tested on benchmarks such as R2R, RxR, REVERIE, VLN-CE, and HM3D, with growing attention to object goals, outdoor and aerial settings, and real-robot deployment. A central challenge is turning correct language and scene understanding into safe, efficient behavior over long routes, where localization drift, changing scenes, limited compute, and the gap between simulation and reality can cause failures.