Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems This paper was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments. To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge fusion algorithm for upgrading a shared model deployed on the cloud. Then, effective transfer learning methods in LFRL are introduced. LFRL is consistent with human cognitive science and fits well in cloud robotic systems. Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation. The cloud robotic system deployment also shows that LFRL is capable of fusing prior knowledge. In addition, we release a cloud robotic navigationlearning website based on LFRL.

FCSLAM: Federated Learning Enhanced Distributed VisualLiDAR SLAM In Cloud Robotic System SLAM has shown great values in many fields such as selfdriving cars, virtual reality and robotic localization, etc. Cloud robot based collaborative SLAM can effectively improve the efficiency of mapping tasks. However, place recognition and matching accuracy among different robots can greatly affect the map fusion performance of the entire SLAM system. Therefore, this paper presents a learning architecture for cooperative SLAM named FCSLAM, a distributed SLAM in cloud robotic systems by taking advantage of federated learning to enhance the performance of visualLiDAR SLAM. Additionally, we propose a federated deep learning algorithm for feature extraction and dynamic vocabulary designation which works in realtime on cloud workstation. FCSLAM can ensure realtime collaborative SLAM by keep the original images on robot side instead of sending them to the cloud server. We test our system on open datasets and in simulated environment. The results show that it has better feature extraction performance than SIFT and ORB under illumination and viewpoint changes. Besides, map fusion is conducted to generate a global map according to place matching relation of distributed robots.