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Distribution-Transfer Safe-Horizon MPC under Mode Uncertainty — arXiv:2610.07277

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.07277
License (as archived): CC0-1.0
Archived: 2026-10-07T13:11:46.513Z
SHA-256 of the archived text: baec0a7e979c1b20cf2bcc7ff47386d6dc5f5ef7428c923712c5b3079696f575
Stable URL of this page: https://metraipolis.com/documento/dc_ec79f343a8e5

How to cite

Distribution-Transfer Safe-Horizon MPC under Mode Uncertainty — arXiv:2610.07277 — arXiv (en), https://arxiv.org/abs/2610.07277 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ec79f343a8e5 (Metraipolis archive dc_ec79f343a8e5, sha256 baec0a7e979c1b20cf2bcc7ff47386d6dc5f5ef7428c923712c5b3079696f575)

BibTeX

@misc{dcec79f343a8e5,
  title = {Distribution-Transfer Safe-Horizon MPC under Mode Uncertainty — arXiv:2610.07277},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.07277},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ec79f343a8e5; sha256 baec0a7e979c1b20cf2bcc7ff47386d6dc5f5ef7428c923712c5b3079696f575},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.07277. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Scenario-based MPC is an attractive strategy for chance-constrained motion planning that approximates uncertainty via a finite set of sampled scenarios. As a sampling-based method, scenario-based MPC is sensitive to distribution mismatch. We address this problem in the context of Safe-Horizon Model Predictive Control (SH-MPC) with obstacles governed by switching dynamic modes. From finite mode observations, we construct a confidence set for the unknown categorical mode law and derive a multiplicative domination bound that transfers a Safe-Horizon collision-risk certificate from a selected scenario-sampling distribution to every law in the confidence set. Wasserstein geometry is used to regularize probability reallocation among modes according to the similarity of their induced trajectory predictions, while a collision-risk surrogate biases sampling toward dangerous modes. The resulting certificate explicitly quantifies the additional tightening required under distribution mismatch and exposes the multiplicative conservatism that arises when several obstacle-wise transfer factors are combined

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.RO, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO) ; Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2610.07277 — 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 2026-10-07T12:42:33.510Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).