MCNav builds a dynamic cognitive map with goal re-validation and missed-goal re-exploration to reach state-of-the-art results on instance-level zero-shot navigation in HM3D environments.
OpenFMNav: Towards open-set zero-shot object navigation via vision-language foundation models
9 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 9representative citing papers
NORM-Nav is a zero-shot framework that parses natural language behavioral constraints with an LLM, grounds them via vision-LiDAR, and encodes them as multi-layer costmaps for grid-based robot navigation.
PLMD applies a denoising diffusion model to predict labels for unknown map regions, allowing goal localization in unexplored environments by substituting completed labels into existing navigation pipelines.
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.
FSUNav's dual brain-inspired modules achieve state-of-the-art zero-shot goal navigation across heterogeneous robots with improved speed, safety, and generalization.
C-Nav is a continual visual navigation framework with dual-path anti-forgetting via feature distillation and replay plus adaptive sampling that outperforms baselines on a new continual object navigation benchmark while using less memory.
Uni-NaVid unifies diverse embodied navigation tasks into one video-based vision-language-action model trained on 3.6 million samples from four sub-tasks, achieving state-of-the-art performance on benchmarks and real-world tests.
NaVid, a video-based VLM trained on 510k navigation and 763k web samples, achieves SOTA VLN performance using only monocular RGB video for next-step action planning in sim and real environments.
CLUE adaptively weights room-type and object-co-location cues from an LLM to construct a unified semantic value map that improves success rate and efficiency in zero-shot object-goal navigation.
citing papers explorer
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MCNav: Memory-Aware Dynamic Cognitive Map for Zero-shot Goal-oriented Navigation
MCNav builds a dynamic cognitive map with goal re-validation and missed-goal re-exploration to reach state-of-the-art results on instance-level zero-shot navigation in HM3D environments.
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NORM-Nav: Zero-Shot Mobile Robot Navigation with Natural Language Behavioral Constraints
NORM-Nav is a zero-shot framework that parses natural language behavioral constraints with an LLM, grounds them via vision-LiDAR, and encodes them as multi-layer costmaps for grid-based robot navigation.
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Plug-and-Play Label Map Diffusion for Universal Goal-Oriented Navigation
PLMD applies a denoising diffusion model to predict labels for unknown map regions, allowing goal localization in unexplored environments by substituting completed labels into existing navigation pipelines.
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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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FSUNav: A Cerebrum-Cerebellum Architecture for Fast, Safe, and Universal Zero-Shot Goal-Oriented Navigation
FSUNav's dual brain-inspired modules achieve state-of-the-art zero-shot goal navigation across heterogeneous robots with improved speed, safety, and generalization.
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C-NAV: Towards Self-Evolving Continual Object Navigation in Open World
C-Nav is a continual visual navigation framework with dual-path anti-forgetting via feature distillation and replay plus adaptive sampling that outperforms baselines on a new continual object navigation benchmark while using less memory.
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Uni-NaVid: A Video-based Vision-Language-Action Model for Unifying Embodied Navigation Tasks
Uni-NaVid unifies diverse embodied navigation tasks into one video-based vision-language-action model trained on 3.6 million samples from four sub-tasks, achieving state-of-the-art performance on benchmarks and real-world tests.
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NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation
NaVid, a video-based VLM trained on 510k navigation and 763k web samples, achieves SOTA VLN performance using only monocular RGB video for next-step action planning in sim and real environments.
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CLUE: Adaptively Prioritized Contextual Cues by Leveraging a Unified Semantic Map for Effective Zero-Shot Object-Goal Navigation
CLUE adaptively weights room-type and object-co-location cues from an LLM to construct a unified semantic value map that improves success rate and efficiency in zero-shot object-goal navigation.