Aerial VLN

Aerial VLN studies how drones interpret language instructions, recognize relevant places or objects from onboard cameras, and navigate to them through open environments. Approaches range from modular vision-language models with spatial maps and memory to end-to-end vision-language-action policies, world models, and diffusion-based trajectory planners; many combine slower task reasoning with faster control. Benchmarks such as CityNav, TravelUAV, and OpenFly test instruction following across simulated scenes, unseen environments, and real flights. A central challenge is grounding long-horizon instructions reliably while maintaining safe, continuous control under viewpoint changes, limited onboard compute, and the gap between simulation and reality.