Abstract
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.
Abstract (translated)
本研究提出了一种新型方法来协助下肢康复,即双代理器多模型强化学习(DAMMRL)框架,结合了多个模型自适应控制(MMAC)和协同自适应控制策略。在机器人辅助康复中,一个关键挑战是由于人类认知和生理系统的复杂性而建模人类行为。传统的单模型方法通常无法捕捉到人机交互的动态。我们的研究采用了一种多模型策略,使用简单的子模型来近似康复任务中的人体反应,适应该患者无能水平的不同程度。该系统具有灵活性,在真实实验和模拟环境中得到了验证。通过使用13名健康年轻受试者进行评估,该研究不仅为机器人辅助下肢康复引入了一种新的范例,而且为未来的自适应、以患者为中心的治疗干预研究打开了道路。
URL
https://arxiv.org/abs/2407.21734
