ProCompNav improves success rate and shortens user responses in ambiguous instance navigation by using comparative binary questions that prune a candidate pool rather than requesting detailed descriptions.
arXiv preprint arXiv:2401.02695 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
FineCog-Nav uses fine-grained cognitive modules driven by foundation models to outperform zero-shot baselines in UAV navigation and introduces the AerialVLN-Fine benchmark with refined instructions.
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.
citing papers explorer
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Proactive Instance Navigation with Comparative Judgment for Ambiguous User Queries
ProCompNav improves success rate and shortens user responses in ambiguous instance navigation by using comparative binary questions that prune a candidate pool rather than requesting detailed descriptions.
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FineCog-Nav: Integrating Fine-grained Cognitive Modules for Zero-shot Multimodal UAV Navigation
FineCog-Nav uses fine-grained cognitive modules driven by foundation models to outperform zero-shot baselines in UAV navigation and introduces the AerialVLN-Fine benchmark with refined instructions.
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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.