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Concurrent Semantic Search and Mission Execution for LTL Missions in Unknown Environments — arXiv:2609.39153

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.39153
License (as archived): CC0-1.0
Archived: 2026-10-07T15:11:55.757Z
SHA-256 of the archived text: 0ad0f74a9a21c6363ffe54acc901ff8082e73bec7f19f01d4d73b95f38990be1
Stable URL of this page: https://metraipolis.com/documento/dc_dec37bf618c2

How to cite

Concurrent Semantic Search and Mission Execution for LTL Missions in Unknown Environments — arXiv:2609.39153 — arXiv (en), https://arxiv.org/abs/2609.39153 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dec37bf618c2 (Metraipolis archive dc_dec37bf618c2, sha256 0ad0f74a9a21c6363ffe54acc901ff8082e73bec7f19f01d4d73b95f38990be1)

BibTeX

@misc{dcdec37bf618c2,
  title = {Concurrent Semantic Search and Mission Execution for LTL Missions in Unknown Environments — arXiv:2609.39153},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.39153},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dec37bf618c2; sha256 0ad0f74a9a21c6363ffe54acc901ff8082e73bec7f19f01d4d73b95f38990be1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.39153. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Planning complex missions in unknown environments requires robots to reason simultaneously about what they should do and what they still need to discover. Existing approaches for solving LTLf missions typically assume a known environment, or separate the exploration of the environment from the execution of the mission, while semantic exploration methods look for one target at a time and ignore the mission being executed. To fill this gap, our main contribution is an adaptive high-level planning method that interleaves a task-driven semantic search with the execution of the mission, advancing both in a non-myopic manner. Our method leverages two representations built online, a metric-semantic scene graph, built with a Vision Language Model (VLM), that provides the evidence needed to locate the objects the mission refers to, and the deterministic finite automaton (DFA) encoding the mission, that indicates which of them matter at each mission state. At every planning stage, our planner selects the waypoints that are most valuable for both the semantic search and the advancement of the mission, valuing them over the remaining mission stages in order to avoid blocking states. The selected waypoints are then ordered in a single high-level plan, which is recomputed as new information arrives. In photorealistic indoor environments over five mission types, our method completes more missions than the compared approaches while having to cover less of the environment, and it does so with shorter paths and complying with the restrictions imposed by the mission.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-10-01 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.39153 — 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 2026-10-07T14:44:47.421Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-10-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).