[Submitted on 7 Oct 2026]
Title:Speaking the Navigator's Language: Trajectory-Grounded Instruction Translation for Frozen Aerial VLN Agents
View a PDF of the paper titled Speaking the Navigator's Language: Trajectory-Grounded Instruction Translation for Frozen Aerial VLN Agents, by Xi Chen and 10 other authors
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Abstract: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 \emph{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 \textbf{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 ($11.33\%{\rightarrow}32.51\%$); it also improves held-out OpenFly ($4.95\%{\rightarrow}20.79\%$) and yields recovery on CityNav and AirVLN.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.10635 [cs.AI]
(or arXiv:2610.10635v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.10635
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xi Chen [view email] [v1] Wed, 7 Oct 2026 13:52:37 UTC (316 KB)
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