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WareFly-VLA: A Vision-Language-Action Framework for UAV Navigation and Human Tracking in Smart Warehouses — arXiv:2610.08526

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.08526
License (as archived): CC0-1.0
Archived: 2026-10-07T13:07:59.962Z
SHA-256 of the archived text: de6d214f3bf00713d61e0694976e5a3a46c08b39cffc2e81383117566898911c
Stable URL of this page: https://metraipolis.com/documento/dc_02ffb0c96ff2

How to cite

WareFly-VLA: A Vision-Language-Action Framework for UAV Navigation and Human Tracking in Smart Warehouses — arXiv:2610.08526 — arXiv (en), https://arxiv.org/abs/2610.08526 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_02ffb0c96ff2 (Metraipolis archive dc_02ffb0c96ff2, sha256 de6d214f3bf00713d61e0694976e5a3a46c08b39cffc2e81383117566898911c)

BibTeX

@misc{dc02ffb0c96ff2,
  title = {WareFly-VLA: A Vision-Language-Action Framework for UAV Navigation and Human Tracking in Smart Warehouses — arXiv:2610.08526},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.08526},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_02ffb0c96ff2; sha256 de6d214f3bf00713d61e0694976e5a3a46c08b39cffc2e81383117566898911c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.08526. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Vision-Language-Action (VLA) models have achieved impressive results in robotic manipulation and ground-mobile navigation, yet language-conditioned control of unmanned aerial vehicles (UAVs) in smart warehouses remains largely unexplored, hindered by the lack of benchmarks that jointly provide continuous low-level flight actions, fine-grained natural-language target descriptions, and realistic industrial environments. This paper introduces WareFly-VLA, a photorealistic UAV VLA framework and dataset for language-guided human search, localization, and tracking in warehouse environments. It contains 507 human-teleoperated flight episodes and 8,504 high-resolution RGB transitions collected in NVIDIA Isaac Sim, each paired with a human-written appearance description of the target worker and a synchronized four-degree-of-freedom control command. Two aerial tasks are covered: target approach and person following, under occlusion, long-range search, altitude variation, and clutter. A unified benchmark of four open-source VLA architectures (SmolVLA, GR00T N1.7, pi_0 and OpenVLA) is established under a leakage-free episode-level protocol at two control rates. The results show that language-conditioned aerial control in warehouses is far from solved: performance drops substantially under strict generalization settings, continuous action modeling consistently outperforms discrete action tokenization, only the forward channel is reliably learnable from a single frame, and current foundation-model interfaces transfer poorly from ground and humanoid embodiments to aerial platforms. The synchronized video, language, action, pose, and difficulty annotations further support world-model research. The dataset, baselines, and evaluation protocol are released to support language-grounded aerial autonomy in smart warehouses.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2610.08526 — 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 2026-10-07T12:37:33.048Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).