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SearchWorld: Spatial Value-Grounded Imagination for UAV Object Search via World Models — arXiv:2610.09335

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.09335
License (as archived): CC0-1.0
Archived: 2026-10-08T11:09:12.799Z
SHA-256 of the archived text: 118bd2ba5c81e5e4f024730ee8fd6d5f89e0dc25bc60d6fb806a3ac294ca9907
Stable URL of this page: https://metraipolis.com/documento/dc_ff0bedc2e2ef

How to cite

SearchWorld: Spatial Value-Grounded Imagination for UAV Object Search via World Models — arXiv:2610.09335 — arXiv (en), https://arxiv.org/abs/2610.09335 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ff0bedc2e2ef (Metraipolis archive dc_ff0bedc2e2ef, sha256 118bd2ba5c81e5e4f024730ee8fd6d5f89e0dc25bc60d6fb806a3ac294ca9907)

BibTeX

@misc{dcff0bedc2e2ef,
  title = {SearchWorld: Spatial Value-Grounded Imagination for UAV Object Search via World Models — arXiv:2610.09335},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.09335},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ff0bedc2e2ef; sha256 118bd2ba5c81e5e4f024730ee8fd6d5f89e0dc25bc60d6fb806a3ac294ca9907},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.09335. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Autonomous unmanned aerial vehicle (UAV) object search involves a closed loop of perception, decision-making, and action under partial observability. Urban environments pose several challenges: large search areas and narrow egocentric views limit coverage, dense 3D geometry constrains safe motion, and open-world instructions require identifying a specific target among distractors. Many existing methods mitigate partial observability through explicit maps or memory representations, yet remain largely reactive, reasoning over past observations without explicitly predicting future states. World models enable prospective reasoning through imagined rollouts. However, image-generating world models can incur high inference latency, while spatially grounded planning remains challenging for latent world models. We propose SearchWorld, a recurrent state-space world model that connects explicit spatial memory with value-guided imagination. The model maintains BEV exploration and obstacle memory and decodes a task-aware spatial value layer to guide search. A cognition-action network uses this learned spatial value prior to improve the policy through imagined rollouts, without training a separate scalar critic. Training progresses from world-model learning to expert imitation and imagination-based exploration refinement. On UAV-ON, SearchWorld improves the success rate to 23.8% (19.5% for the strongest published agent) and raises oracle success to 35.5%, while remaining robust on unseen scenes (19.9% success rate). By grounding imagination in explicit spatial representations, SearchWorld enables UAV agents to plan prospectively rather than react.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.09335 — 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 2026-10-08T10:14:05.926Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).