CoNav-UAV: Cooperative Dual-Altitude Aerial Navigation via Stackelberg Learning

2026-08-03

AI Summary

This work addresses the challenge that a single unmanned aerial vehicle (UAV) struggles to simultaneously achieve global exploration and safe, close-proximity target approach in vision-and-language navigation (VLN). To this end, the authors propose a novel high–low altitude dual-UAV cooperative system, introducing Stackelberg game theory into this domain for the first time. Their approach enables mutual adaptive learning using only onboard visual and linguistic inputs, without reliance on privileged information. The method employs an iterative Stackelberg learning framework, integrating memory-based contextual reasoning to refine the high-level policy and leveraging DAgger-style expert distillation to enhance low-level control. Evaluated on the AerialVLN benchmark, the proposed system substantially outperforms existing single- and multi-agent baselines, achieving up to a 30.8% absolute improvement in seen-scenario success rate, a 9.0% gain in cross-scenario generalization, and requiring only approximately one-third of the adaptation data.

Abstract

Target-oriented vision-and-language navigation (VLN) on aerial platforms is attracting growing attention for missions such as disaster rescue, infrastructure inspection, and security patrol. In this task, an unmanned aerial vehicle (UAV) needs to locate targets given only a concise description of their appearance and surroundings. This requires global exploration and grounding as well as collision-free close-range approach, two interleaved processes difficult to reconcile within a single agent. Most existing methods transfer the ground VLN paradigm to a low-altitude UAV and compensate for its inefficient exploration with external assistance. A recent attempt deploys two UAVs at complementary altitudes yet still relies on privileged information and trains its two agents independently, precluding any mutual adaptation essential for cooperation. Here we propose CoNav-UAV, which explicitly models the task as a Stackelberg game between a high-altitude leader and a low-altitude follower, with the system operating on onboard visual and linguistic inputs alone. To solve this game, we introduce Iterative Stackelberg Learning. The leader's high-level vision-language reasoning is refined via memory-based in-context learning, while the follower's precise motion control is updated via DAgger-style expert distillation. The alternation drives both agents toward a Stackelberg equilibrium. CoNav-UAV consistently outperforms single- and dual-agent baselines across three high-fidelity urban scenes from the AerialVLN benchmark. Success rate improves by up to 30.8 points on the learning scene, and 9.0 points under cross-scene transfer while using about 3x less adaptation data. Further analyses validate the complementary gains of the leader and follower updates and reveal robust gains yet distinct learning dynamics across VLM backbones.

Problem

• vision-and-language navigation
• aerial navigation
• multi-agent cooperation
• UAV
• Stackelberg game

Innovation

• Stackelberg learning
• vision-and-language navigation
• cooperative UAV
• dual-altitude navigation
• onboard perception