基于双智能体多模型强化学习（DAMMRL）的人机协同适应机器人辅助康复
Human-Machine Co-Adaptation for Robot-Assisted Rehabilitation via Dual-Agent Multiple Model Reinforcement Learning (DAMMRL)
AI总结：
本文提出双智能体多模型强化学习（DAMMRL）框架，结合多模型自适应控制与协同自适应策略，用于机器人辅助踝关节康复，并通过13名健康受试者实验验证了其可行性与潜力。
摘要：
本研究提出了一种新的机器人辅助踝关节康复方法，通过提出双智能体多模型强化学习（DAMMRL）框架，利用多模型自适应控制（MMAC）和协同自适应控制策略。在机器人辅助康复中，由于人类认知和生理系统的复杂性，建模人类行为是关键挑战之一。传统的单模型方法往往无法捕捉人机交互的动态特性。本研究采用多模型策略，使用简单的子模型来近似康复任务中复杂的人类反应，并根据患者不同程度的活动能力进行定制。所提系统的通用性在真实实验和仿真环境中得到了验证。研究对13名健康年轻受试者进行了可行性和潜力评估，取得了有前景的结果，证实了该方法的预期益处。本研究不仅为机器人辅助踝关节康复引入了一种新范式，也为未来以患者为中心的自适应治疗干预研究开辟了道路。
英文摘要：
This study introduces a novel approach to robot-assisted ankle rehabilitation by proposing a Dual-Agent Multiple Model Reinforcement Learning (DAMMRL) framework, leveraging multiple model adaptive control (MMAC) and co-adaptive control strategies. In robot-assisted rehabilitation, one of the key challenges is modelling human behaviour due to the complexity of human cognition and physiological systems. Traditional single-model approaches often fail to capture the dynamics of human-machine interactions. Our research employs a multiple model strategy, using simple sub-models to approximate complex human responses during rehabilitation tasks, tailored to varying levels of patient incapacity. The proposed system's versatility is demonstrated in real experiments and simulated environments. Feasibility and potential were evaluated with 13 healthy young subjects, yielding promising results that affirm the anticipated benefits of the approach. This study not only introduces a new paradigm for robot-assisted ankle rehabilitation but also opens the way for future research in adaptive, patient-centred therapeutic interventions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
