Federated reinforcement learning enhanced human-robotic systems: a comprehensive review

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Federated Reinforcement learning (FRL) presents a transformative approach for leveraging Human-robot collaboration (HRC) systems by addressing critical challenges in traditional learning paradigms. This paper provides a comprehensive review of the current state of FRL technology and its potential applications within HRC systems. The adaptation of FRL in HRC system is still in its infancy. This review systematically analyses the development trends, current challenges, and future prospects of various learning approaches within HRC systems. The paper highlights the critical factors of developing a conceptual frame-work for FRL within HRC systems to fully realise the potential of FRL. This paper aims to provide valuable insights and guidance for future research efforts focused on advancing FRL technology for human-robotic collaboration.
Original languageEnglish
Title of host publicationProceedings of the 2024 IEEE International Conference on e-Business Engineering (ICEBE)
EditorsOmar Hussain, Yinsheng Li, Shang-Pin Ma, Xin Lu, Kuo-Ming Chao
PublisherIEEE
Pages145-151
Number of pages7
ISBN (Electronic)9798350365856
ISBN (Print)9798350365863
DOIs
Publication statusPublished - 16 Dec 2024
EventIEEE International Conference on E-Business Engineering 2024 - Fudan University, Shanghai
Duration: 11 Oct 202413 Oct 2024
https://conferences.computer.org/icebe/2024/index.html

Academic conference

Academic conferenceIEEE International Conference on E-Business Engineering 2024
Abbreviated titleICEBE 2024
CityShanghai
Period11/10/2413/10/24
Internet address

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