Poster: FedRos-Federated Reinforcement Learning for Networked Mobile-Robot Collaboration
In this paper, we propose FedRos, a Federated Reinforcement Learning based multi-robot system, which enables networked robots collaboratively to train a shared model without sharing their private sensing data. Firstly, we present the FedRos pipeline that embeds the Webots robotics simulator. We then highlight features of FedRos, including its compatibility with the state-of-The-Art Federated Learning and Reinforcement Learning algorithms and its sim-To-real viability. Lastly, we present benchmark experiments to show the effectiveness of FedRos.11Jianwei Huang and Bing Luo are co-corresponding authors of this paper. This work is supported by the National Natural Science Foundation of China (Project 62271434), Shenzhen Science and Technology Program (Project JCY120210324120011032), Guangdong Basic and Applied Basic Research Foundation (Project 2021B1515120008), Shenzhen Key Lab of Crowd Intelligence Empowered Low-Carbon Energy Network (No. ZDSYS20220606100601002), and the Shenzhen Institute of Artificial Intelligence and Robotics for Society. Video of FedRos demo: https://youtube/oDVpB6eo6qs
