Current vision-language navigation methods face substantial bottlenecks regarding heterogeneous robot compatibility, real-time performance, and navigation safety. Furthermore, they struggle to support open-vocabulary semantic generalization and multimodal task inputs. To address these challenges, this paper proposes FSUNav: a Cerebrum-Cerebellum architecture for fast, safe, and universal zero-shot goal-oriented navigation, which innovatively integrates vision-language models (VLMs) with the proposed architecture. The cerebellum module, a high-frequency end-to-end module, develops a universal local planner based on deep reinforcement learning, enabling unified navigation across heterogeneous platforms (e.g., humanoid, quadruped, wheeled robots) to improve navigation efficiency while significantly reducing collision risk. The cerebrum module constructs a three-layer reasoning model and leverages VLMs to build an end-to-end detection and verification mechanism, enabling zero-shot open-vocabulary goal navigation without predefined IDs and improving task success rates in both simulation and real-world environments. Additionally, the framework supports multimodal inputs (e.g., text, target descriptions, and images), further enhancing generalization, real-time performance, safety, and robustness. Experimental results on MP3D, HM3D, and OVON benchmarks demonstrate that FSUNav achieves state-of-the-art performance on object, instance image, and task navigation, significantly outperforming existing methods. Real-world deployments on diverse robotic platforms further validate its robustness and practical applicability.

翻译：当前视觉语言导航方法在异构机器人兼容性、实时性能和导航安全性方面面临重大瓶颈。此外，它们难以支持开放词汇语义泛化和多模态任务输入。为解决这些挑战，本文提出FSUNav：一种用于快速、安全且通用零样本目标导向导航的大脑-小脑架构，创新性地将视觉语言模型与所提出的架构相结合。小脑模块作为一个高频端到端模块，基于深度强化学习开发了通用局部规划器，能够在异构平台（如人形、四足、轮式机器人）上实现统一导航，以提高导航效率并显著降低碰撞风险。大脑模块构建了三层推理模型，并利用视觉语言模型建立端到端检测与验证机制，实现了无需预定义ID的零样本开放词汇目标导航，在仿真和真实环境中均提高了任务成功率。此外，该框架支持多模态输入（如文本、目标描述和图像），进一步增强了泛化能力、实时性能、安全性和鲁棒性。在MP3D、HM3D和OVON基准上的实验结果表明，FSUNav在物体、实例图像和任务导航上达到了最先进性能，显著优于现有方法。在多种机器人平台上的真实部署进一步验证了其鲁棒性和实际应用价值。