FaB-Fly: Foresight and Backtracking for Aerial Vision-and-Language Navigation

In aerial vision-and-language navigation, an unmanned aerial vehicle (UAV) follows natural-language instructions that specify landmarks and how to move relative to them. Recent memory-based hierarchical approaches retrieve candidate landmark locations from memory and use a planner to select successive targets. However, several locations may match the same landmark description, so the planner must assess whether flying to each candidate would satisfy the instruction. If the UAV moves on after arrival without checking whether its flight followed the instruction, an incorrect choice can lead to further navigation errors. We propose FaB-Fly, a hierarchical navigation framework that uses Foresight to select targets and Backtracking to recover from incorrect target choices. Foresight uses a video world model to generate a flight video for each candidate, based on current observations and a candidate-specific movement instruction. A multimodal large language model (MLLM) scores how well each predicted video matches the corresponding part of the navigation instruction to guide target selection. After execution, Backtracking evaluates the observed flight against the same instruction segment. If the score is low, the UAV returns to the departure point and retries with another candidate. Experiments on AerialVLN-S and AerialVLN-Fine show that FaB-Fly outperforms the strongest baselines in success rate. Further analysis shows that FaB-Fly achieves a success rate close to that of exhaustively visiting every candidate while requiring about half the computation time.