Deliberate Before You Fly: Vision-Guided Spatial Deliberation for UAV See-and-Reach Navigation

• URL: http://arxiv.org/abs/2608.04825v1
• Date: Wed, 05 Aug 2026 13:27:07 GMT
• Title: Deliberate Before You Fly: Vision-Guided Spatial Deliberation for UAV See-and-Reach Navigation
• Abstract summary: See-and-reach navigation requires an aerial agent to approach a language-specified target visible in its initial view and stop reliably near it.<n>Existing methods typically map vision-language representations directly to action outputs without explicitly modeling intermediate fine-grained spatial decisions.<n>We propose DBFly, a vision-language waypoint prediction framework that introduces explicit vision-guided spatial deliberation before waypoint generation.
• Score: 23.72068584471195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: UAV see-and-reach navigation requires an aerial agent to approach a language-specified target visible in its initial view and stop reliably near it. Existing methods typically map vision-language representations directly to action outputs without explicitly modeling intermediate fine-grained spatial decisions. This direct mapping causes semantic-control misalignment, leading to inconsistent maneuvers and unreliable termination. To address this issue, we propose DBFly, a vision-language waypoint prediction framework that introduces explicit vision-guided spatial deliberation before waypoint generation. Specifically, DBFly introduces a spatial maneuver decision chain that progressively performs target-direction anchoring, spatial diagnosis, and maneuver decision, enabling high-level maneuver intent to explicitly guide continuous waypoint generation. DBFly further constructs an implicit flight corridor by transforming the initial target-direction prior into a persistent geometric reference and deriving an online corridor state from the UAV's current position, thereby providing soft geometric guidance for spatial diagnosis and maneuver correction. In addition, DBFly develops a terminal-convergence-aware stopping strategy that characterizes terminal states through both target proximity and short-horizon motion convergence, enabling more reliable stopping near the target. Extensive experiments across seen, unseen-object, and unseen-scene test sets demonstrate that DBFly improves the success rate over the SOTA baseline by an average of 25.07 percentage points. The project homepage is available at https://xuefanfu.github.io/DBFly-Page.

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