FOGL: Federated Object Grasping Learning
Author:
Affiliation:
1. Sungkyunkwan University,Department of Artificial Intelligence,Suwon,Gyeonggi,South Korea
2. University of Minnesota,Department of Electrical and Computer Engineering,Twin Cities, Minneapolis,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10160211/10160212/10161191.pdf?arnumber=10161191
Reference26 articles.
1. Federated Deep Reinforcement Learning for Task Scheduling in Heterogeneous Autonomous Robotic System
2. On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios
3. Tackling the objective inconsistency problem in heterogeneous federated optimization;wang;Advances in neural information processing systems,2020
4. Federated optimization in heterogeneous networks;li;Proceedings of Machine Learning and Systems,0
5. Federated Learning in Robotic and Autonomous Systems
Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Make Federated Learning a Standard in Robotics by Using ROS2;Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies;2023-12-04
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