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FATHOM: World-Model-Guided Diffusion Action-Trajectory Selection for Sparse-Marker AUV Tracking — arXiv:2611.08898

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.08898
License (as archived): CC0-1.0
Archived: 2026-11-09T22:45:56.396Z
SHA-256 of the archived text: 33367f530b30b737918a972c8323951255d455ec1a9bd3e5fc311d7a8b954927
Stable URL of this page: https://metraipolis.com/documento/dc_66fa40871925

How to cite

FATHOM: World-Model-Guided Diffusion Action-Trajectory Selection for Sparse-Marker AUV Tracking — arXiv:2611.08898 — arXiv (en), https://arxiv.org/abs/2611.08898 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_66fa40871925 (Metraipolis archive dc_66fa40871925, sha256 33367f530b30b737918a972c8323951255d455ec1a9bd3e5fc311d7a8b954927)

BibTeX

@misc{dc66fa40871925,
  title = {FATHOM: World-Model-Guided Diffusion Action-Trajectory Selection for Sparse-Marker AUV Tracking — arXiv:2611.08898},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.08898},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_66fa40871925; sha256 33367f530b30b737918a972c8323951255d455ec1a9bd3e5fc311d7a8b954927},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.08898. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Reliable relative tracking is fundamental to cooperative autonomous underwater vehicles (AUVs), yet remains challenging under nonlinear vehicle dynamics and degraded underwater visual conditions. Active light markers improve robustness to appearance variation, but provide deliberately sparse observations that can become incomplete or corrupted by occlusion, reflections, and detection noise during dynamic motion. To address the resulting partial-observation control problem, this paper proposes FATHOM, a world-model-guided diffusion action-trajectory selection framework for sparse-marker AUV tracking. FATHOM first integrates structured marker observations, inertial measurements, action history, and temporal dynamics into a predictive tracking belief using a multimodal task-supervised recurrent state-space model. A diffusion action prior then generates multiple expert-like multi-thruster trajectories, whose future tracking consequences are predicted through the learned latent dynamics. Rather than directly executing a generated trajectory, FATHOM selects the most favorable candidate under an explicit receding-horizon objective and continually updates the decision from onboard observations. Extensive offline analyses verify the learned predictive belief, multi-step world-model prediction, diffusion trajectory generation, and predictive selection. More importantly, comprehensive real-AUV experiments, including strong pose-based comparisons and component ablations, demonstrate robust autonomous tracking across nominal and challenging motions with naturally occurring partial marker visibility, while maintaining real-time onboard operation without motion-capture feedback.

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/2611.08898 — 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 2026-11-09T22:38:02.166Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).