LOVON：腿式开放词汇目标导航器
LOVON: Legged Open-Vocabulary Object Navigator
AI总结：
本文提出LOVON框架，结合大语言模型与开放词汇检测，解决动态非结构化环境中腿式机器人的长距离目标导航问题，并通过多种机器人实验验证其有效性与即插即用性。
摘要：
在开放世界环境中进行目标导航对机器人系统而言仍是一个艰巨且普遍的挑战，尤其是在执行需要开放世界目标检测和高级任务规划的长时程任务时。传统方法往往难以有效整合这些组件，这限制了它们处理复杂、长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，它将用于分层任务规划的大型语言模型（LLMs）与开放词汇视觉检测模型相结合，专为在动态、非结构化环境中进行有效的长距离目标导航而设计。为了应对包括视觉抖动、盲区和临时目标丢失在内的现实世界挑战，我们设计了专门的解决方案，例如用于视觉稳定的拉普拉斯方差滤波。我们还为机器人开发了一种功能性执行逻辑，以保证LOVON在自主导航、任务适应和稳健任务完成方面的能力。大量评估证明了涉及实时检测、搜索和朝向开放词汇动态目标导航的长序列任务的成功完成。此外，在不同腿式机器人（Unitree Go2、B2和H1-2）上进行的现实世界实验展示了LOVON的兼容性和有吸引力的即插即用特性。
英文摘要：
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.
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。
