SAP-Nav: Spatial Semantic Representation Meets Active Perception for Hierarchical Open-Vocabulary Object Navigation
SAP-Nav is a fully online, zero-shot framework for hierarchical open-vocabulary object navigation. It incrementally builds a queryable spatial-semantic representation from actively acquired room views and uses active viewpoint verification to reposition the agent when evidence is insufficient. It achieves the best overall performance on LangMap and HM3D-OVON, with a 12.2% SR improvement on region-level navigation over training-based methods, and is validated in real-world robot experiments.
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[Submitted on 13 Aug 2026]
Title:SAP-Nav: Spatial Semantic Representation Meets Active Perception for Hierarchical Open-Vocabulary Object Navigation
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Abstract:Hierarchical open-vocabulary object navigation (OVON) requires agents to follow free-form instructions that may specify targets through scene-, room-, region-, and instance-level cues in unseen environments. Although recent work LangMap has formalized this setting, reliably solving it under partial observations remains challenging: spatial grounding requires persistent environment-level evidence, whereas target verification requires clear and discriminative candidate views. We present SAP-Nav, a fully online, zero-shot framework that addresses both requirements through active perception. SAP-Nav incrementally constructs a Queryable Spatial-Semantic Representation from actively acquired room views, enabling spatial semantic queries from any explored location. It further employs Active Viewpoint Verification to assess whether the current observation provides sufficient evidence and, when necessary, reposition the agent to a more informative viewpoint before verifying candidates against category and attribute constraints. Although designed for hierarchical OVON, SAP-Nav supports both hierarchical and standard category-level OVON without task-specific training or precomputed scene maps. Experiments on LangMap and HM3D-OVON show that SAP-Nav achieves the overall best performance, including a 12.2% improvement in SR over training-based methods on region-level navigation. Real-world robot experiments further demonstrate its practical feasibility. Code will be made publicly available upon acceptance.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2608.12707 [cs.RO]
(or arXiv:2608.12707v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.12707
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xuetong Pei [view email] [v1] Thu, 13 Aug 2026 01:43:18 UTC (2,373 KB)
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