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VoroNav: Voronoi-based Zero-shot Object Navigation with Large Language Model
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In the realm of household robotics, the Zero-Shot Object Navigation (ZSON) task empowers agents to adeptly traverse unfamiliar environments and locate objects from novel categories without prior explicit training. This paper introduces VoroNav, a novel semantic exploration framework that proposes the Reduced Voronoi Graph to extract exploratory paths and planning nodes from a semantic map constructed in real time. By harnessing topological and semantic information, VoroNav designs text-based descriptions of paths and images that are readily interpretable by a large language model (LLM). In particular, our approach presents a synergy of path and farsight descriptions to represent the environmental context, enabling LLM to apply commonsense reasoning to ascertain waypoints for navigation. Extensive evaluation on HM3D and HSSD validates VoroNav surpasses existing benchmarks in both success rate and exploration efficiency (absolute improvement: +2.8% Success and +3.7% SPL on HM3D, +2.6% Success and +3.8% SPL on HSSD). Additionally introduced metrics that evaluate obstacle avoidance proficiency and perceptual efficiency further corroborate the enhancements achieved by our method in ZSON planning. Project page: https://voro-nav.github.io
Forward citations
Cited by 15 Pith papers
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VTM-Nav: Harnessing Cross-Episode Experience for Object-Goal Navigation with Hierarchical Visual-Topological Memory
A hierarchical room-and-object memory that persists across independent ObjectNav episodes yields small success-rate gains, but most of the gain comes from within-episode memory rather than the cross-episode component.
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DRIVE-Nav: Directional Reasoning, Inspection, and Verification for Efficient Open-Vocabulary Navigation
Organizing zero-shot object navigation around tracked directional exits with 240° inspection and VLM verification yields 50.2% SR / 32.6% SPL on HM3D-OVON and best SPL on HM3Dv2 and MP3D.
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Relational Semantic Reasoning on 3D Scene Graphs for Open World Interactive Object Search
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Visual-Language-Guided Task Planning for Horticultural Robots
A vision-language model drives a simulated greenhouse robot through simple crop-inspection tasks with ~87% success, but long multi-target tasks collapse to under 10% success.
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PIGEON: VLM-Driven Object Navigation via Points of Interest Selection
PIGEON selects sparse Points of Interest for VLM-driven object navigation, achieving state-of-the-art zero-shot ObjectNav results in Habitat and improving a local 7B VLM via distance-based RLVR.
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Imaginative World Modeling with Scene Graphs for Embodied Agent Navigation
SGImagineNav uses an imagined hierarchical scene graph, filled in by an LLM, that guides a robot to unseen objects and achieves 65.4% and 66.8% success on HM3D and HSSD.
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MAG-Nav: Language-Driven Object Navigation Leveraging Memory-Reserved Active Grounding
MAG-Nav uses active viewpoint selection and memory replay with GPT-4o to achieve state-of-the-art 40.8% success in zero-shot language-driven object navigation on GOAT-Bench/HM3D.
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HRO: Hierarchical Room-to-Object Framework for Zero-Shot Object Goal Navigation with Large Language Models
Hierarchical LLM room-type inference plus object-room affinity scoring yields 84.0% Gibson and 53.0% HM3D success for zero-shot object-goal navigation.
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Curriculum-Based Multi-Tier Semantic Exploration via Deep Reinforcement Learning
A curriculum-trained DRL agent with a VLM query action and layered rewards is claimed to improve semantic exploration and object discovery in AI2-THOR.
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TopoNav: Topological Graphs as a Key Enabler for Advanced Object Navigation
A zero-shot object navigation system that builds a text-based topological memory graph, queried by GPT-4o, reports state-of-the-art success rates of 60.1% on HM3D and 45.5% on MP3D.
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CogDDN: A Cognitive Demand-Driven Navigation with Decision Optimization and Dual-Process Thinking
CogDDN uses a fast heuristic VLM paired with a slow analytic reflection process and a growing knowledge base to navigate to objects that implicitly satisfy a user's demand, with large reported gains on AI2Thor DDN benchmarks.
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BeliefMapNav: 3D Voxel-Based Belief Map for Zero-Shot Object Navigation
A zero-shot navigation system that builds a 3D voxel belief map from LLM-generated landmarks and CLIP features, then plans frontier paths by expected search distance, achieving state-of-the-art success rate and succes...
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CogNav: Cognitive Process Modeling for Object Goal Navigation with LLMs
CogNav improves object goal navigation by having an LLM decide transitions between five cognitive states, guided by an online cognitive map, and reports state-of-the-art success rates on HM3D, MP3D, and RoboTHOR.
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Grounded Vision-Language Navigation for UAVs with Open-Vocabulary Goal Understanding
VLFly integrates LLaMA, CLIP, and a pretrained ViNT waypoint planner to guide a drone by matching language instructions to goal images, reporting 83% success on direct and 70% on indirect real-world instructions.
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Explainability for Vision Foundation Models: A Survey
A structured review of 122 papers on explainability for vision foundation models, with a taxonomy and the finding that quantitative evaluation is rare (36%).
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