REVIEW 13 cited by
WMNav: Integrating Vision-Language Models into World Models for Object Goal Navigation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
WMNav: Integrating Vision-Language Models into World Models for Object Goal Navigation
read the original abstract
Object Goal Navigation-requiring an agent to locate a specific object in an unseen environment-remains a core challenge in embodied AI. Although recent progress in Vision-Language Model (VLM)-based agents has demonstrated promising perception and decision-making abilities through prompting, none has yet established a fully modular world model design that reduces risky and costly interactions with the environment by predicting the future state of the world. We introduce WMNav, a novel World Model-based Navigation framework powered by Vision-Language Models (VLMs). It predicts possible outcomes of decisions and builds memories to provide feedback to the policy module. To retain the predicted state of the environment, WMNav proposes the online maintained Curiosity Value Map as part of the world model memory to provide dynamic configuration for navigation policy. By decomposing according to a human-like thinking process, WMNav effectively alleviates the impact of model hallucination by making decisions based on the feedback difference between the world model plan and observation. To further boost efficiency, we implement a two-stage action proposer strategy: broad exploration followed by precise localization. Extensive evaluation on HM3D and MP3D validates WMNav surpasses existing zero-shot benchmarks in both success rate and exploration efficiency (absolute improvement: +3.2% SR and +3.2% SPL on HM3D, +13.5% SR and +1.1% SPL on MP3D). Project page: https://b0b8k1ng.github.io/WMNav/.
Forward citations
Cited by 13 Pith papers
-
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.
-
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.
-
Turning Adaptation into Assets: Cross-Domain Bridging for Online Vision-Language Navigation
IDEA is a TTA framework for VLN that builds a dynamic asset library from Fisher-weighted soft prompts and domain coordinates, then uses convex-hull projection for cross-domain bridging and training-free adaptation.
-
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.
-
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.
-
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.
-
FiLM-Nav: Efficient and Generalizable Navigation via VLM Fine-tuning
FiLM-Nav fine-tunes VLMs on a mixture of simulated navigation tasks to reach state-of-the-art SPL and success on HM3D ObjectNav and OVON benchmarks with generalization to unseen categories.
-
HoloAgent-0: A Unified Embodied Agent Framework with 3D Spatial Memory
HoloAgent-0 is a unified embodied agent framework with Embodied AgentOS, 3D spatial memory, and embodied skills, deployed and evaluated on real robot hardware for navigation and manipulation tasks.
-
Qwen-RobotNav Technical Report: A Scalable Navigation Model Designed for an Agentic Navigation System
Qwen-RobotNav introduces a parameterized navigation model supporting multiple task modes and controllable observation parameters, trained on 15.6M samples with vision-language co-training to achieve SOTA results on be...
-
Qwen-RobotNav Technical Report: A Scalable Navigation Model Designed for an Agentic Navigation System
Qwen-RobotNav provides a parameterized navigation model trained on 15.6M samples with vision-language co-training that achieves SOTA results on benchmarks and zero-shot transfer to real robots.
-
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...
-
Spatiotemporal Knowledge Graphs as Persistent Scene Memory for Embodied Question Answering
A training-free pipeline constructs a spatiotemporal knowledge graph from egocentric video, enabling low-latency, explainable embodied question answering.
-
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.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.