标题： LOVON：有腿的开放词汇物体导航者 显示英文标题

标题： LOVON: Legged Open-Vocabulary Object Navigator

摘要： 在开放世界环境中进行物体导航仍然是机器人系统面临的严峻且普遍的挑战，特别是在执行需要同时进行开放世界物体检测和高层任务规划的长时程任务时。 传统方法往往难以有效地整合这些组件，这限制了它们处理复杂、远距离导航任务的能力。 在本文中，我们提出了LOVON，一种新颖的框架，将大语言模型（LLMs）用于分层任务规划，并与开放词汇视觉检测模型相结合，专门用于在动态、非结构化环境中进行有效的远距离物体导航。 为应对包括视觉抖动、盲区和临时目标丢失在内的现实挑战，我们设计了专用解决方案，例如用于视觉稳定的拉普拉斯方差过滤。 我们还开发了机器人的功能执行逻辑，确保LOVON在自主导航、任务适应和鲁棒任务完成方面的能力。 广泛的评估表明，成功完成了涉及实时检测、搜索和向开放词汇动态目标进行导航的长序列任务。 此外，在不同腿式机器人（Unitree Go2、B2和H1-2）上的实际实验展示了LOVON的兼容性和吸引人的即插即用特性。 显示英文摘要

摘要： Object navigation in open-world environments remains a formidable and pervasive challenge for robotic systems, particularly when it comes to executing long-horizon tasks that require both open-world object detection and high-level task planning. Traditional methods often struggle to integrate these components effectively, and this limits their capability to deal with complex, long-range navigation missions. In this paper, we propose LOVON, a novel framework that integrates large language models (LLMs) for hierarchical task planning with open-vocabulary visual detection models, tailored for effective long-range object navigation in dynamic, unstructured environments. To tackle real-world challenges including visual jittering, blind zones, and temporary target loss, we design dedicated solutions such as Laplacian Variance Filtering for visual stabilization. We also develop a functional execution logic for the robot that guarantees LOVON's capabilities in autonomous navigation, task adaptation, and robust task completion. Extensive evaluations demonstrate the successful completion of long-sequence tasks involving real-time detection, search, and navigation toward open-vocabulary dynamic targets. Furthermore, real-world experiments across different legged robots (Unitree Go2, B2, and H1-2) showcase the compatibility and appealing plug-and-play feature of LOVON.