SFCo-Nav: 通过慢LLM与快速属性图对齐的协作实现高效的零样本视觉语言导航
SFCo-Nav: Efficient Zero-Shot Visual Language Navigation via Collaboration of Slow LLM and Fast Attributed Graph Alignment
AI总结：
SFCo-Nav通过慢LLM与快速属性图对齐的协作，实现了高效的零样本视觉语言导航，显著提升效率并降低计算成本。
摘要：
近年来，大型视觉语言模型（VLMs）和大型语言模型（LLMs）的进展使零样本视觉语言导航（VLN）成为可能，其中智能体仅使用自身感知和推理来遵循自然语言指令。然而，现有零样本方法通常构建一个简单的观察图，并对其进行每步VLM-LLM推理，导致高延迟和计算成本，限制了实时部署。为了解决这一问题，我们提出了SFCo-Nav，一种高效的零样本VLN框架，灵感来自慢-快认知协作的原则。SFCo-Nav集成了三个关键模块：1）一个基于慢LLM的规划器，生成一系列子目标链，每个子目标都与想象的对象图相关联；2）一个快速的反应导航器，用于实时构建对象图并执行子目标；3）一个轻量级的异步慢-快桥梁，将高级结构化、属性化的想象和感知图对齐，以估计导航信心，仅在必要时触发慢LLM规划器。据我们所知，SFCo-Nav是首个支持根据内部信心异步触发LLM的慢-快协作零样本VLN系统。在公开的R2R和REVERIE基准上评估，SFCo-Nav在匹配或超过现有最先进零样本VLN成功率的同时，将每条轨迹的总token消耗减少超过50%，并运行速度超过3.5倍。最后，我们在酒店套房中的腿部机器人上展示了SFCo-Nav，展示了其在室内环境中的效率和实用性。
英文摘要：
Recent advances in large vision-language models (VLMs) and large language models (LLMs) have enabled zero-shot approaches to visual language navigation (VLN), where an agent follows natural language instructions using only ego perception and reasoning. However, existing zero-shot methods typically construct a naive observation graph and perform per-step VLM-LLM inference on it, resulting in high latency and computation costs that limit real-time deployment. To address this, we present SFCo-Nav, an efficient zero-shot VLN framework inspired by the principle of slow-fast cognitive collaboration. SFCo-Nav integrates three key modules: 1) a slow LLM-based planner that produces a strategic chain of subgoals, each linked to an imagined object graph; 2) a fast reactive navigator for real-time object graph construction and subgoal execution; and 3) a lightweight asynchronous slow-fast bridge aligns advanced structured, attributed imagined and perceived graphs to estimate navigation confidence, triggering the slow LLM planner only when necessary. To the best of our knowledge, SFCo-Nav is the first slow-fast collaboration zero-shot VLN system supporting asynchronous LLM triggering according to the internal confidence. Evaluated on the public R2R and REVERIE benchmarks, SFCo-Nav matches or exceeds prior state-of-the-art zero-shot VLN success rates while cutting total token consumption per trajectory by over 50% and running more than 3.5 times faster. Finally, we demonstrate SFCo-Nav on a legged robot in a hotel suite, showcasing its efficiency and practicality in indoor environments.
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。
