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

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dc.contributor.authorUpadhyay, Anurag
dc.contributor.authorAbafat, Soheil
dc.contributor.authorBaradaranshokouhi, Yashar
dc.contributor.authorLu, Xin
dc.contributor.authorJing, Yanguo
dc.contributor.authorLi, Jun
dc.date.accessioned2025-02-17T13:39:13Z
dc.date.available2025-02-17T13:39:13Z
dc.date.freetoread2025-02-17
dc.date.issued2024-10-11
dc.date.pubOnline2024-12-16
dc.description.abstractFederated 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.
dc.description.conferencename2024 IEEE International Conference on e-Business Engineering (ICEBE)
dc.format.extentpp. 145-151
dc.identifier.citationUpadhyay A, Abafat S, Baradaranshokouhi Y, et al., (2024) Federated reinforcement learning enhanced human-robotic systems: a comprehensive review. In: 2024 IEEE International Conference on e-Business Engineering (ICEBE), 11-13 Oct 2024, Shanghai, China
dc.identifier.elementsID561639
dc.identifier.urihttps://doi.org/10.1109/icebe62490.2024.00031
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23485
dc.language.isoen
dc.publisherIEEE
dc.publisher.urihttps://ieeexplore.ieee.org/document/10784435
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject46 Information and Computing Sciences
dc.subject4608 Human-Centred Computing
dc.subject40 Engineering
dc.titleFederated reinforcement learning enhanced human-robotic systems: a comprehensive review
dc.typeConference paper
dcterms.coverageShanghai, China
dcterms.dateAccepted2024-08-21
dcterms.temporal.endDate13 Oct 2024
dcterms.temporal.startDate11 Oct 2024

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