Human-Machine Co-Adaptation for Robot-Assisted Rehabilitation via Dual-Agent Multiple Model Reinforcement Learning (DAMMRL)
arxiv(2024)
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.
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