Guided Diffusion with Distilled Vision-Language Reliability for Aerial Navigation
Researchers propose a reliability-aware diffusion planner that distills a vision-language model to generate scene-level reliability heatmaps, guiding UAVs to avoid unreliable regions (e.g., glass, mirrors) in 3D navigation, reducing obstacle violation rate from 40.3% to 9.6% and raising mean reliability from 0.588 to 0.925.
[2606.13883] Guided Diffusion with Distilled Vision-Language Reliability for Aerial Navigation
[Submitted on 11 Jun 2026]
Title:Guided Diffusion with Distilled Vision-Language Reliability for Aerial Navigation
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Abstract:Autonomous UAV navigation is conventionally solved by pipelines that separate perception, mapping, and planning into distinct stages, which propagates errors, accumulates latency, and requires environment-specific retuning. End-to-end generative models remove these interfaces by mapping raw observations directly to trajectories, but inherit a subtle failure mode: trained on clean data, they cannot recognise when an observation is unreliable, and treat degraded regions such as glass, mirrors, and overexposed surfaces as valid evidence for planning. We present a reliability-aware diffusion planner for 3D UAV navigation. It conditions trajectory generation on the observation together with a scene-level reliability heatmap that marks where perception cannot be trusted, produced by a lightweight network that distils the open-vocabulary reasoning of a vision-language model within the real-time planning budget. To generalise to unseen environments without retraining, we steer the denoising process with a differentiable two-stage ESDF cost that treats physical obstacles from depth and virtual obstacles from highly unreliable regions on equal footing. In simulation and on a real quadrotor, our planner produces markedly safer trajectories than a state-of-the-art diffusion baseline, reducing the obstacle-violation rate from 40.3% to 9.6% and raising the mean reliability of traversed regions from 0.588 to 0.925. Ablating the reliability term alone drops mean reliability from 0.898 to 0.783, confirming it as the decisive component, while distillation runs the framework up to 2 times faster than the full vision-language model.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.13883 [cs.RO]
(or arXiv:2606.13883v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.13883
arXiv-issued DOI via DataCite (pending registration)
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From: Ivan Valuev [view email] [v1] Thu, 11 Jun 2026 20:18:34 UTC (1,694 KB)
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