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. We present CogNav, which attempts to model this cognitive process with the help of large language models. 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:
• 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.

Related papers

• Mem2Ego: Empowering Vision-Language Models with Global-to-Ego Memory for Long-Horizon Embodied Navigation [35.71602601385161]
  We present a novel vision-language model (VLM)-based navigation framework. Our approach enhances spatial reasoning and decision-making in long-horizon tasks. Experimental results demonstrate that the proposed method surpasses previous state-of-the-art approaches in object navigation tasks.
  arXiv  Detail & Related papers  (2025-02-20T04:41:40Z)
• Cog-GA: A Large Language Models-based Generative Agent for Vision-Language Navigation in Continuous Environments [19.818370526976974]
  Vision Language Navigation in Continuous Environments (VLN-CE) represents a frontier in embodied AI. We introduce Cog-GA, a generative agent founded on large language models (LLMs) tailored for VLN-CE tasks. Cog-GA employs a dual-pronged strategy to emulate human-like cognitive processes.
  arXiv  Detail & Related papers  (2024-09-04T08:30:03Z)
• Learning Navigational Visual Representations with Semantic Map Supervision [85.91625020847358]
  We propose a navigational-specific visual representation learning method by contrasting the agent's egocentric views and semantic maps. Ego$2$-Map learning transfers the compact and rich information from a map, such as objects, structure and transition, to the agent's egocentric representations for navigation.
  arXiv  Detail & Related papers  (2023-07-23T14:01:05Z)
• How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers [94.46825166907831]
  We present a training-free solution to tackle the object goal navigation problem in Embodied AI. Our method builds a structured scene representation based on the classic visual simultaneous localization and mapping (V-SLAM) framework. Our method propagates semantics on the scene graphs based on language priors and scene statistics to introduce semantic knowledge to the geometric frontiers.
  arXiv  Detail & Related papers  (2023-05-26T13:38:33Z)
• ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation [75.13546386761153]
  We present a novel zero-shot object navigation method, Exploration with Soft Commonsense constraints (ESC) ESC transfers commonsense knowledge in pre-trained models to open-world object navigation without any navigation experience. Experiments on MP3D, HM3D, and RoboTHOR benchmarks show that our ESC method improves significantly over baselines.
  arXiv  Detail & Related papers  (2023-01-30T18:37:32Z)
• Navigating to Objects in the Real World [76.1517654037993]
  We present a large-scale empirical study of semantic visual navigation methods comparing methods from classical, modular, and end-to-end learning approaches. We find that modular learning works well in the real world, attaining a 90% success rate. In contrast, end-to-end learning does not, dropping from 77% simulation to 23% real-world success rate due to a large image domain gap between simulation and reality.
  arXiv  Detail & Related papers  (2022-12-02T01:10:47Z)
• Towards self-attention based visual navigation in the real world [0.0]
  Vision guided navigation requires processing complex visual information to inform task-orientated decisions. Deep Reinforcement Learning agents trained in simulation often exhibit unsatisfactory results when deployed in the real-world. This is the first demonstration of a self-attention based agent successfully trained in navigating a 3D action space, using less than 4000 parameters.
  arXiv  Detail & Related papers  (2022-09-15T04:51:42Z)
• PONI: Potential Functions for ObjectGoal Navigation with Interaction-free Learning [125.22462763376993]
  We propose Potential functions for ObjectGoal Navigation with Interaction-free learning (PONI) PONI disentangles the skills of where to look?' for an object and how to navigate to (x, y)?'
  arXiv  Detail & Related papers  (2022-01-25T01:07:32Z)
• Occupancy Anticipation for Efficient Exploration and Navigation [97.17517060585875]
  We propose occupancy anticipation, where the agent uses its egocentric RGB-D observations to infer the occupancy state beyond the visible regions. By exploiting context in both the egocentric views and top-down maps our model successfully anticipates a broader map of the environment. Our approach is the winning entry in the 2020 Habitat PointNav Challenge.
  arXiv  Detail & Related papers  (2020-08-21T03:16:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.