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SOR-Nav: Search or Relocate? Context-Gated Exploration and Cross-Region Relocation for Object Navigation — arXiv:2609.34707

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.34707
License (as archived): CC0-1.0
Archived: 2026-10-08T00:17:28.049Z
SHA-256 of the archived text: 4aa6a70daa287cc28a1e74fc58ffe9e463189e7a385ec03f1b4449c50c7289ac
Stable URL of this page: https://metraipolis.com/documento/dc_9bfea42ca7ff

How to cite

SOR-Nav: Search or Relocate? Context-Gated Exploration and Cross-Region Relocation for Object Navigation — arXiv:2609.34707 — arXiv (en), https://arxiv.org/abs/2609.34707 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_9bfea42ca7ff (Metraipolis archive dc_9bfea42ca7ff, sha256 4aa6a70daa287cc28a1e74fc58ffe9e463189e7a385ec03f1b4449c50c7289ac)

BibTeX

@misc{dc9bfea42ca7ff,
  title = {SOR-Nav: Search or Relocate? Context-Gated Exploration and Cross-Region Relocation for Object Navigation — arXiv:2609.34707},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.34707},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_9bfea42ca7ff; sha256 4aa6a70daa287cc28a1e74fc58ffe9e463189e7a385ec03f1b4449c50c7289ac},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.34707. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Object navigation requires an embodied agent to find an object in an unseen environment under partial observability and a limited motion budget. Existing methods primarily optimize where the robot should go next by ranking candidate destinations. In contrast to these methods, we present SOR-Nav, a hierarchical navigation system that explicitly arbitrates between continuing to explore the current context and abandoning it for a more promising reachable region. First, an autonomous semantic exploration system is built that accumulates persistent 3D object clusters and organizes reachable frontiers into a cluster decision graph to provide an efficient search abstraction. Then, SOR-Nav uses a context-gated LLM-driven object-search supervisor to evaluate the suitability of the current search context and decide whether to continue exploration or perform cross-region relocation to another reachable frontier cluster. Across the complete, unfiltered validation sets of HM3D-v1, HM3D-v2, and MP3D, SOR-Nav achieves the strongest reported Success Rate (SR) and Success weighted by Path Length (SPL) on all three benchmarks. On MP3D in particular, it more than doubles the previous best SPL from 18.1\% to 38.5\% while increasing SR from 50.7\% to 61.8\%. Nested HM3D-v2 ablations validate the proposed decision structure, while a continuous three-target physical deployment demonstrates persistent ObjectNav operation in real-world scenarios.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-29 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.34707 — 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 2026-10-07T23:52:16.573Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).