Cited by 242Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot LearningRobotics and Automated Systems

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Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems

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Liu, Boyi, Wang, Lujia, Liu, Ming·2019·IEEE Robotics and Automation Letters

DOI 10.1109/lra.2019.2931179

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Abstract

This letter 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 letter, 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 navigation-learning website to provide the service based on LFRL: www.shared-robotics.com.

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A Hybrid Federated Reinforcement Learning Approach for Networked Robots
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Authors: Liu, B., Wang, L., & Liu, M.
Affiliations: Shenzhen Institutes of Advanced Technology (CN)
Hong Kong University of Science and Technology (HK)
ORCID: Liu, Boyi
Wang, Lujia
Liu, Ming
Venue: IEEE Robotics and Automation Letters
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2019-10
Volume: 4
Issue: 4
Pages: 4555-4562
ISSN: 2377-3766 (ISSN-L)
2377-3774
DOI: 10.1109/lra.2019.2931179
OpenAlex: W2911450448
Cited by: 242
References: 36
Citations by year: 2026: 5 · 2025: 29 · 2024: 38 · 2023: 40 · 2022: 33 · 2021: 50 · 2020: 27 · 2019: 20
FWCI: 15.00
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Robotics and Automation Letters (publishedVersion)
arXiv (Cornell University) (submittedVersion)
Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
arXiv (Cornell University)
Funders: Shenzhen Science and Technology Innovation Commission (JCYJ2017081853518789)
Guangdong Science and Technology Plan Guangdong-Hong Kong Cooperation Innovation Platform (2018B050502009)
National Natural Science Foundation of China (61603376, U1713211)
Shenzhen Science, Technology and Innovation Commission (JCYJ20160428154842603)
Basic Research Project of Shanghai Science and Technology Commission (16JC1401200)
Chengzhong Xu from University of Macau
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Engineering › Control and Systems Engineering › Robotics and Automated Systems
Keywords: lifelong learning, federated reinforcement learning, robot navigation, cloud robotics, knowledge fusion, transfer learning

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Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems

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