Speaking the Navigator's Language: Trajectory-Grounded Instruction Translation for Frozen Aerial VLN Agents

Aerial vision-and-language navigation (VLN) agents are typically trained on detail-rich, trajectory-aligned commands, whereas users issue short, intent-driven instructions; on a frozen OpenFly navigator, this instruction gap drops success rate (SR) from 31.03% to 11.33%. To scale translator training, we prompt a language model with human-written style examples to convert original commands into paired, intent-centered Weak commands, which yield 15.27% SR. We introduce the Trajectory-Grounded Instruction Translator (TGIT), a front-end that keeps the navigator frozen and translates Weak inputs into agent-executable commands by learning from its trajectory outcomes. The resulting Weak-trained translator raises Weak-input SR to 37.93% and transfers zero-shot to real human instructions (11.33%rightarrow32.51%); it also improves held-out OpenFly (4.95%rightarrow20.79%) and yields recovery on CityNav and AirVLN.