OVAL introduces an open-vocabulary memory model with structured descriptors and multi-value frontier scoring to enable efficient lifelong object goal navigation in unseen settings.
Bridging zero-shot object navigation and foundation models through pixel-guided navigation skill
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 3years
2026 3representative citing papers
ReMemNav improves zero-shot object navigation success and efficiency by integrating episodic memory and rethinking with VLMs, achieving SR/SPL gains of 1.7%/7.0% on HM3D v0.1, 18.2%/11.1% on HM3D v0.2, and 8.7%/7.9% on MP3D.
Introduces a hierarchical VLN architecture with asynchronous layers, incremental memory graph, and WTRP-based exploration that improves success and efficiency on resource-constrained robots.
citing papers explorer
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OVAL: Open-Vocabulary Augmented Memory Model for Lifelong Object Goal Navigation
OVAL introduces an open-vocabulary memory model with structured descriptors and multi-value frontier scoring to enable efficient lifelong object goal navigation in unseen settings.
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ReMemNav: A Rethinking and Memory-Augmented Framework for Zero-Shot Object Navigation
ReMemNav improves zero-shot object navigation success and efficiency by integrating episodic memory and rethinking with VLMs, achieving SR/SPL gains of 1.7%/7.0% on HM3D v0.1, 18.2%/11.1% on HM3D v0.2, and 8.7%/7.9% on MP3D.
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A Deployable Embodied Vision-Language Navigation System with Hierarchical Cognition and Context-Aware Exploration
Introduces a hierarchical VLN architecture with asynchronous layers, incremental memory graph, and WTRP-based exploration that improves success and efficiency on resource-constrained robots.