Robotics: Science and Systems XIX

Demonstrating Arena-Web: A Web-based Development and Benchmarking Platform for Autonomous Navigation Approaches

Linh Kästner, Reyk Carstens, Lena Nahrwold, Christopher Liebig, Volodymyr Shcherbyna, Subhin Lee, Jens Lambrecht

Abstract:

In recent years, mobile robot navigation approaches have become increasingly important due to various application areas ranging from healthcare to warehouse logistics. In particular, Deep Reinforcement Learning approaches have gained popularity for robot navigation but are not easily accessible to non-experts and complex to develop. In recent years, efforts have been made to make these sophisticated approaches accessible to a wider audience. In this paper, we present Arena-Web, a web-based development and evaluation suite for developing, training, and testing DRL-based navigation planners for various robotic platforms and scenarios. The interface is designed to be intuitive and engaging to appeal to non-experts and make the technology accessible to a wider audience. With Arena-Web and its interface, training and developing Deep Reinforcement Learning agents is simplified and made easy without a single line of code. The web-app is free to use and openly available under the link stated in the supplementary materials.

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Bibtex:

  
@INPROCEEDINGS{Kästner-RSS-23, 
    AUTHOR    = {Linh Kästner AND Reyk Carstens AND Lena Nahrwold AND Christopher  Liebig AND Volodymyr Shcherbyna AND Subhin Lee AND Jens Lambrecht}, 
    TITLE     = {{Demonstrating Arena-Web: A Web-based Development and Benchmarking Platform for Autonomous Navigation Approaches}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2023}, 
    ADDRESS   = {Daegu, Republic of Korea}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2023.XIX.088} 
}