基于反射的无先验知识开放词汇导航：使用全向相机与多个视觉-语言模型
Reflex-Based Open-Vocabulary Navigation without Prior Knowledge Using Omnidirectional Camera and Multiple Vision-Language Models
AI总结：
本研究提出一种无需先验地图或学习的开放词汇机器人导航方法，利用全向相机和多个预训练视觉-语言模型结合反射行为实现简单导航，并在移动机器人Fetch上验证了其特性与局限。
摘要：
已有多种机器人导航方法被开发，但它们主要基于同步定位与建图（SLAM）、强化学习等，需要先验地图构建或学习。本研究考虑最简单的方法，无需任何地图构建或学习，并在无任何先验知识的情况下执行机器人的开放词汇导航。我们将全向相机和预训练视觉-语言模型应用于机器人。全向相机提供周围环境的均匀视图，从而消除了包括轨迹生成在内的复杂探索行为的需要。通过将多个预训练视觉-语言模型应用于该全向图像，并结合反射行为，我们表明导航变得简单且不需要任何先验设置。基于移动机器人Fetch的实验，我们讨论了本方法的有趣特性和局限性。
英文摘要：
Various robot navigation methods have been developed, but they are mainly based on Simultaneous Localization and Mapping (SLAM), reinforcement learning, etc., which require prior map construction or learning. In this study, we consider the simplest method that does not require any map construction or learning, and execute open-vocabulary navigation of robots without any prior knowledge to do this. We applied an omnidirectional camera and pre-trained vision-language models to the robot. The omnidirectional camera provides a uniform view of the surroundings, thus eliminating the need for complicated exploratory behaviors including trajectory generation. By applying multiple pre-trained vision-language models to this omnidirectional image and incorporating reflective behaviors, we show that navigation becomes simple and does not require any prior setup. Interesting properties and limitations of our method are discussed based on experiments with the mobile robot Fetch.
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。
