Arena-Web -- A Web-based Development and Benchmarking Platform for Autonomous Navigation Approaches
This addresses the accessibility issue for non-experts in robotics and AI, though it is incremental as it builds on existing efforts to democratize DRL tools.
The paper tackles the problem of making Deep Reinforcement Learning (DRL) for robot navigation accessible to non-experts by presenting Arena-Web, a web-based platform that simplifies training and development without coding, resulting in a free and openly available tool.
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.