Browsing by Author "Jing, Yanguo"

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    Federated reinforcement learning enhanced human-robotic systems: a comprehensive review
    (IEEE, 2024-10-11) Upadhyay, Anurag; Abafat, Soheil; Baradaranshokouhi, Yashar; Lu, Xin; Jing, Yanguo; Li, Jun
    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.