FSUNav: 一种用于快速、安全和通用零样本目标导向导航的脑皮层-小脑架构
FSUNav: A Cerebrum-Cerebellum Architecture for Fast, Safe, and Universal Zero-Shot Goal-Oriented Navigation
AI总结：
本文提出FSUNav架构，通过整合视觉语言模型与小脑-脑皮层模块，实现跨异构平台的零样本目标导航，提升效率与安全性。
摘要：
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存在显著瓶颈，且难以支持开放词汇语义泛化和多模态任务输入。为此，本文提出FSUNav：一种用于快速、安全和通用零样本目标导向导航的脑皮层-小脑架构，创新性地将视觉语言模型与该架构结合。小脑模块通过深度强化学习开发通用局部规划器，实现跨异构平台（如人形、四足、轮式机器人）的统一导航，提高导航效率并显著降低碰撞风险。脑皮层模块构建三层推理模型，并利用视觉语言模型构建端到端的检测和验证机制，实现无需预定义ID的零样本开放词汇目标导航，提升仿真和现实环境中的任务成功率。此外，该框架支持多模态输入（如文本、目标描述和图像），进一步增强泛化能力、实时性能、安全性和鲁棒性。在MP3D、HM3D和OVON基准测试中，FSUNav在物体、实例图像和任务导航上取得最佳性能，显著优于现有方法。在多种机器人平台上的实际部署进一步验证了其鲁棒性和实用性。
英文摘要：
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.
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。
