Metraipolis › archive › dc_87fa4cedf3ca

Learning to Fly: Stable Vision-Guided UAV Servoing with Compact Target-Centric Cues and Reinforcement Learning — arXiv:2609.09234

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.09234
License (as archived): CC0-1.0
Archived: 2026-10-16T11:11:27.843Z
SHA-256 of the archived text: 146d5d5e640dd2b53d7985ca933d98474b6b5e485f663f0493a0c50498579865
Stable URL of this page: https://metraipolis.com/documento/dc_87fa4cedf3ca

How to cite

Learning to Fly: Stable Vision-Guided UAV Servoing with Compact Target-Centric Cues and Reinforcement Learning — arXiv:2609.09234 — arXiv (en), https://arxiv.org/abs/2609.09234 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_87fa4cedf3ca (Metraipolis archive dc_87fa4cedf3ca, sha256 146d5d5e640dd2b53d7985ca933d98474b6b5e485f663f0493a0c50498579865)

BibTeX

@misc{dc87fa4cedf3ca,
  title = {Learning to Fly: Stable Vision-Guided UAV Servoing with Compact Target-Centric Cues and Reinforcement Learning — arXiv:2609.09234},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.09234},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_87fa4cedf3ca; sha256 146d5d5e640dd2b53d7985ca933d98474b6b5e485f663f0493a0c50498579865},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.09234. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Vision-guided reinforcement learning for Unmanned Aerial Vehicles (UAVs) remains challenging due to unstable policy optimisation, aggressive exploration, and the cost of high-dimensional visual perception. In this work, we investigate long-horizon UAV visual servoing using compact target-centric cues combined with low-dimensional sensor measurements. Rather than learning directly from RGB images, lightweight target segmentation provides image-space offsets and relative depth, which are combined with quadrotor velocity and projected-gravity measurements into a compact 12D policy observation. We compare Direct PPO with three matched-budget curriculum strategies: a Visual curriculum that progressively expands target placement difficulty, a Dynamics curriculum that gradually relaxes action constraints and smoothing, and a Joint curriculum that combines both progressions. All strategies reach comparable nominal performance, with complementary advantages across tracking metrics. Observation ablations show that proprioceptive measurements are critical for stable flight and image-space cues for target alignment, while explicit depth is not necessary for strong performance in the evaluated setting. Against tuned classical visual-servo controllers, learned policies show greater robustness to strong control and visual perturbations, while the Visual curriculum exhibits the smallest degradation under unseen target motion. Overall, the results demonstrate that compact target-centric representations can support robust long-horizon aerial visual servoing and that visual curriculum training can improve robustness to dynamic distribution shifts despite limited gains in nominal performance.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-10 in cs.RO).
Categorie arXiv: Robotics (cs.RO) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.09234 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T10:19:11.870Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-10?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).