ConsistNav is a new training-free framework that uses a semantic executive controller, persistent candidate memory, and stability-aware action control to close the action consistency gap in zero-shot object navigation, reporting SOTA results on HM3D and MP3D with 11.4% SR and 7.9% SPL gains on MP3D.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
A learned context-energy term in port-Hamiltonian policies creates selective risk navigation that activates evasive forces only when safer paths are available.
citing papers explorer
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ConsistNav: Closing the Action Consistency Gap in Zero-Shot Object Navigation with Semantic Executive Control
ConsistNav is a new training-free framework that uses a semantic executive controller, persistent candidate memory, and stability-aware action control to close the action consistency gap in zero-shot object navigation, reporting SOTA results on HM3D and MP3D with 11.4% SR and 7.9% SPL gains on MP3D.
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Learning Material-Aware Hamiltonian Risk Fields for Safe Navigation
A learned context-energy term in port-Hamiltonian policies creates selective risk navigation that activates evasive forces only when safer paths are available.