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CogNav: Cognitive Process Modeling for Object Goal Navigation with LLMs
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CogNav: Cognitive Process Modeling for Object Goal Navigation with LLMs
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Object goal navigation (ObjectNav) is a fundamental task in embodied AI, requiring an agent to locate a target object in previously unseen environments. This task is particularly challenging because it requires both perceptual and cognitive processes, including object recognition and decision-making. While substantial advancements in perception have been driven by the rapid development of visual foundation models, progress on the cognitive aspect remains constrained, primarily limited to either implicit learning through simulator rollouts or explicit reliance on predefined heuristic rules. Inspired by neuroscientific findings demonstrating that humans maintain and dynamically update fine-grained cognitive states during object search tasks in novel environments, we propose CogNav, a framework designed to mimic this cognitive process using large language models. Specifically, we model the cognitive process using a finite state machine comprising fine-grained cognitive states, ranging from exploration to identification. Transitions between states are determined by a large language model based on a dynamically constructed heterogeneous cognitive map, which contains spatial and semantic information about the scene being explored. Extensive evaluations on the HM3D, MP3D, and RoboTHOR benchmarks demonstrate that our cognitive process modeling significantly improves the success rate of ObjectNav at least by relative 14% over the state-of-the-arts.
Forward citations
Cited by 8 Pith papers
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LocalNav: Distilling Frontier VLMs and Embodied RL for On-Device Object Goal Navigation
Distillation from frontier VLMs plus E-RLVR regularization produces a 4B local model that achieves 34.5% SR on OVON while cutting inference latency by 82.8%.
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SAGE-Nav: Leveraging LLM Planning and Alignment Fusion for Hierarchical Scene Graph-Guided Navigation
SAGE-Nav decouples LLM global planning from reactive control via hierarchical scene graphs and alignment fusion, reporting SOTA results on i-THOR and RoboTHOR with improved efficiency and zero-shot generalization.
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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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HiRO-Nav: Hybrid ReasOning Enables Efficient Embodied Navigation
HiRO-Nav adaptively triggers reasoning only on high-entropy actions via a hybrid training pipeline and shows better success-token trade-offs than always-reason or never-reason baselines on the CHORES-S benchmark.
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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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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.
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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...
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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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