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SOR-Nav introduces a hierarchical navigation framework that leverages an LLM-driven supervisor to dynamically decide between local exploration and cross-region relocation for embodied agents. By explicitly arbitrating search contexts, the system significantly improves success rates and path efficiency on standard benchmarks like MP3D while demonstrating persistent operation in real-world deployments. This advancement marks a notable step forward in autonomous robotics by integrating semantic reasoning with motion planning under partial observability.
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SOR-Nav: Search or Relocate? Context-Gated Exploration and Cross-Region Relocation for Object Navigation

arXiv3 min read

arXiv:2609.34707v1 Announce Type: new Abstract: 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.

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

Abstract: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.

Robotics (cs.RO)

arXiv:2609.34707 [cs.RO]

(or arXiv:2609.34707v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.34707 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 307 words

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