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arXiv 2409.02522cs.AIcs.RO
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Cog-GA:一种用于连续环境中视觉语言导航的基于大语言模型的生成式智能体

Cog-GA: A Large Language Models-based Generative Agent for Vision-Language Navigation in Continuous Environments

Institute of Automation, Chinese Academy of Science (CASIA)(中国科学院自动化研究所);University of Chinese Academy of Sciences (UCAS)(中国科学院大学);The University of Hong Kong(香港大学)

Zhiyuan Li, Yanfeng Lu, Yao Mu, Hong Qiao

AI总结:

针对连续环境视觉语言导航的多模态理解与决策挑战,提出基于大语言模型的生成式智能体Cog-GA,通过构建认知地图、航点预测、双通道场景描述与反思机制实现类人导航,达到SOTA性能并提升可解释性。

摘要:

连续环境中的视觉语言导航(VLN-CE)代表了具身AI的前沿领域,要求智能体仅凭自然语言指令在无界3D空间中自由导航。该任务在多模态理解、空间推理和决策方面带来了独特挑战。为应对这些挑战,我们引入了Cog-GA,这是一种基于大语言模型(LLMs)构建的生成式智能体,专为VLN-CE任务定制。Cog-GA采用双管齐下的策略来模拟类人的认知过程。首先,它构建了一个认知地图,整合时间、空间和语义元素,从而促进LLMs中空间记忆的发展。其次,Cog-GA采用航点预测机制,战略性地优化探索轨迹以最大化导航效率。每个航点都配有双通道场景描述,将环境线索分为“是什么”和“在哪里”的信息流,类似于大脑的机制。这种分离增强了智能体的注意力焦点,使其能够辨别与导航相关的空间信息。反思机制通过捕获先前导航经验的反馈来补充这些策略,促进持续学习和自适应重新规划。在VLN-CE基准上进行的广泛评估验证了Cog-GA的最先进性能及其模拟类人导航行为的能力。这项研究对开发具有策略性和可解释性的VLN-CE智能体做出了重大贡献。

英文摘要:

Vision Language Navigation in Continuous Environments (VLN-CE) represents a frontier in embodied AI, demanding agents to navigate freely in unbounded 3D spaces solely guided by natural language instructions. This task introduces distinct challenges in multimodal comprehension, spatial reasoning, and decision-making. To address these challenges, 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. Firstly, it constructs a cognitive map, integrating temporal, spatial, and semantic elements, thereby facilitating the development of spatial memory within LLMs. Secondly, Cog-GA employs a predictive mechanism for waypoints, strategically optimizing the exploration trajectory to maximize navigational efficiency. Each waypoint is accompanied by a dual-channel scene description, categorizing environmental cues into 'what' and 'where' streams as the brain. This segregation enhances the agent's attentional focus, enabling it to discern pertinent spatial information for navigation. A reflective mechanism complements these strategies by capturing feedback from prior navigation experiences, facilitating continual learning and adaptive replanning. Extensive evaluations conducted on VLN-CE benchmarks validate Cog-GA's state-of-the-art performance and ability to simulate human-like navigation behaviors. This research significantly contributes to the development of strategic and interpretable VLN-CE agents.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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