  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.

Robotics;Computer Vision and Pattern Recognition