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

TL;DR

This paper presents a novel dual-agent reinforcement learning framework for robot-assisted ankle rehabilitation, effectively modeling complex human responses and demonstrating promising results in experiments with healthy subjects.

Contribution

It introduces DAMMRL, a new adaptive control framework combining multiple models to better capture human behavior in rehabilitation tasks.

Findings

Successful implementation in experiments with healthy subjects

Demonstrated versatility in real and simulated environments

Indicated potential for personalized rehabilitation strategies

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.