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BeliefMapNav: 3D Voxel-Based Belief Map for Zero-Shot Object Navigation
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BeliefMapNav: 3D Voxel-Based Belief Map for Zero-Shot Object Navigation
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Zero-shot object navigation (ZSON) allows robots to find target objects in unfamiliar environments using natural language instructions, without relying on pre-built maps or task-specific training. Recent general-purpose models, such as large language models (LLMs) and vision-language models (VLMs), equip agents with semantic reasoning abilities to estimate target object locations in a zero-shot manner. However, these models often greedily select the next goal without maintaining a global understanding of the environment and are fundamentally limited in the spatial reasoning necessary for effective navigation. To overcome these limitations, we propose a novel 3D voxel-based belief map that estimates the target's prior presence distribution within a voxelized 3D space. This approach enables agents to integrate semantic priors from LLMs and visual embeddings with hierarchical spatial structure, alongside real-time observations, to build a comprehensive 3D global posterior belief of the target's location. Building on this 3D voxel map, we introduce BeliefMapNav, an efficient navigation system with two key advantages: i) grounding LLM semantic reasoning within the 3D hierarchical semantics voxel space for precise target position estimation, and ii) integrating sequential path planning to enable efficient global navigation decisions. Experiments on HM3D, MP3D, and HSSD benchmarks show that BeliefMapNav achieves state-of-the-art (SOTA) Success Rate (SR) and Success weighted by Path Length (SPL), with a notable 46.4% SPL improvement over the previous best SR method, validating its effectiveness and efficiency.
Forward citations
Cited by 8 Pith papers
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ProCompNav: Proactive Instance Navigation with Comparative Judgment for Ambiguous User Queries
ProCompNav disambiguates ambiguous instance navigation queries via candidate-pool construction followed by attribute-based comparative binary questions that prune distractors, yielding higher success rates and shorter...
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ProCompNav: 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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Hierarchical 3D Scene Graph Construction and Belief-based Planning for Semantic Navigation
Proposes online hierarchical 3D scene graph construction paired with belief-based planning to improve zero-shot semantic navigation performance in unseen environments.
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EvoMemNav: Efficient Self-Evolving Fine-Grained Memory for Zero-Shot Embodied Navigation
EvoMemNav builds a Visual-Semantic Memory Graph keeping raw views, applies a budgeted coarse-to-fine policy, and uses reflection-driven updates to improve zero-shot navigation on GOAT-Bench and HM3D.
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ProCompNav: Proactive Instance Navigation with Comparative Judgment for Ambiguous User Queries
ProCompNav builds a candidate pool from ambiguous queries then uses pool-splitting binary questions for disambiguation, improving success rate and shortening responses on CoIN-Bench and TextNav.
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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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HIMM: Human-Inspired Long-Term Memory Modeling for Embodied Exploration and Question Answering
An embodied-agent memory framework that disentangles episodic and semantic memories, retrieves past experiences via visual reasoning, and distills program-style rules achieves new state-of-the-art results on A-EQA and...
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IntentNav: Learning Spatial-Visual Object Navigation from Human Demonstrations
IntentNav is a spatial-visual imitation framework that infers human search intent via frontier labeling to train VLM policies for object navigation, reporting SOTA on MP3D and HM3D benchmarks with zero-shot transfer t...
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