CogNav: Cognitive Process Modeling for Object Goal Navigation with LLMs

• URL: http://arxiv.org/abs/2412.10439v1
• Date: Wed, 11 Dec 2024 09:50:35 GMT
• Title: CogNav: Cognitive Process Modeling for Object Goal Navigation with LLMs
• Authors: Yihan Cao, Jiazhao Zhang, Zhinan Yu, Shuzhen Liu, Zheng Qin, Qin Zou, Bo Du, Kai Xu,
• Abstract summary: Object goal navigation (ObjectNav) is a fundamental task of embodied AI that requires the agent to find a target object in unseen environments.<n>We present CogNav, which attempts to model this cognitive process with the help of large language models.<n>In an open-vocabulary and zero-shot setting, our method advances the SOTA of the HM3D benchmark from 69.3% to 87.2%.
• Score: 33.123447047397484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object goal navigation (ObjectNav) is a fundamental task of embodied AI that requires the agent to find a target object in unseen environments. This task is particularly challenging as it demands both perceptual and cognitive processes for effective perception and decision-making. While perception has gained significant progress powered by the rapidly developed visual foundation models, the progress on the cognitive side remains limited to either implicitly learning from massive navigation demonstrations or explicitly leveraging pre-defined heuristic rules. Inspired by neuroscientific evidence that humans consistently update their cognitive states while searching for objects in unseen environments, we present CogNav, which attempts to model this cognitive process with the help of large language models. Specifically, we model the cognitive process with a finite state machine composed of cognitive states ranging from exploration to identification. The transitions between the states are determined by a large language model based on an online built heterogeneous cognitive map containing spatial and semantic information of the scene being explored. Extensive experiments on both synthetic and real-world environments demonstrate that our cognitive modeling significantly improves ObjectNav efficiency, with human-like navigation behaviors. In an open-vocabulary and zero-shot setting, our method advances the SOTA of the HM3D benchmark from 69.3% to 87.2%. The code and data will be released.

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