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Closed-Loop Adaptive Sampling on a Generative Ocean Model — arXiv:2611.05776

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.05776
License (as archived): CC0-1.0
Archived: 2026-11-05T18:30:34.566Z
SHA-256 of the archived text: c6a474890a8cb0e62b502ebcea0b936ebfb4048f822f76a32961dc2651a6faf5
Stable URL of this page: https://metraipolis.com/documento/dc_ebbf83e2d3ac

How to cite

Closed-Loop Adaptive Sampling on a Generative Ocean Model — arXiv:2611.05776 — arXiv (en), https://arxiv.org/abs/2611.05776 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ebbf83e2d3ac (Metraipolis archive dc_ebbf83e2d3ac, sha256 c6a474890a8cb0e62b502ebcea0b936ebfb4048f822f76a32961dc2651a6faf5)

BibTeX

@misc{dcebbf83e2d3ac,
  title = {Closed-Loop Adaptive Sampling on a Generative Ocean Model — arXiv:2611.05776},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.05776},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ebbf83e2d3ac; sha256 c6a474890a8cb0e62b502ebcea0b936ebfb4048f822f76a32961dc2651a6faf5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.05776. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Adaptive sampling can substantially improve the value of sparse ocean observations by directing mobile robots toward measurements that are most informative for an evolving environment. Recent learned generative ocean models can produce high-resolution forecasts and ensembles at substantially lower computational cost than traditional numerical models, creating an opportunity to use rich predictive forecasts directly in closed-loop robotic sampling. However, these models do not natively provide the explicit uncertainty representations assumed by classical informative-planning methods, and their forecasts often evolve in learned latent spaces rather than directly in physical ocean variables. We develop a multi-robot closed-loop adaptive sampling framework that uses a single learned generative model for forecasting, uncertainty estimation, planning, and measurement assimilation. The model forecasts an ensemble in latent space, decodes it to physical ocean states, and uses ensemble statistics to construct A- or D-optimal sampling objectives. We select sampling locations subject to robot reachability under predicted currents. The collected measurements are assimilated back into the latent state using observation-conditioned diffusion sampling. Across 120 test scenarios A- and D-optimal sampling generally outperforms static, random, and systematic-coverage baselines while adding little planning overhead. Our experiments uncover several scenarios in which model uncertainty diverges from forecast error, motivating future work in uncertainty quantification.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.RO, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2611.05776 — 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 2026-11-05T18:20:03.982Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).