Visual Semantic Navigation (VSN) is the ability of a robot to learn visual semantic information for navigating in unseen environments. These VSN models are typically tested in those virtual environments where they are trained, mainly using reinforcement learning based approaches. Therefore, we do not yet have an in-depth analysis of how these models would behave in the real world. In this work, we propose a new solution to integrate VSN models into real robots, so that we have true embodied agents. We also release a novel ROS-based framework for VSN, ROS4VSN, so that any VSN-model can be easily deployed in any ROS-compatible robot and tested in a real setting. Our experiments with two different robots, where we have embedded two state-of-the-art VSN agents, confirm that there is a noticeable performance difference of these VSN solutions when tested in real-world and simulation environments. We hope that this research will endeavor to provide a foundation for addressing this consequential issue, with the ultimate aim of advancing the performance and efficiency of embodied agents within authentic real-world scenarios. Code to reproduce all our experiments can be found at https://github.com/gramuah/ros4vsn.

翻译：视觉语义导航（VSN）是机器人通过学习视觉语义信息在未知环境中进行导航的能力。这些VSN模型通常在与训练环境相同的虚拟场景中测试，且主要采用基于强化学习的方法。因此，我们尚未深入分析这些模型在真实世界中的表现。本研究提出了一种将VSN模型集成到真实机器人的新方案，从而实现了真正的具身智能体。我们还发布了一个基于ROS的新型VSN框架——ROS4VSN，使得任何VSN模型都能轻松部署到兼容ROS的机器人上，并在真实环境中进行测试。我们在两种不同机器人上进行的实验（其中嵌入了两个最先进的VSN智能体）证实，这些VSN解决方案在真实世界与仿真环境中的测试结果存在显著性能差异。我们希望这项研究能为解决这一关键问题奠定基础，最终目标是提升具身智能体在真实场景中的性能与效率。重现所有实验的代码可在https://github.com/gramuah/ros4vsn获取。