mlsanityData to 14 Oct 2026

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

arXiv 2407.21734 ↗First posted 31 Jul 2024cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 55 references that are in the map. First posted on arXiv 31 Jul 2024.

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4 of 55 references.

References · 4

  1. 2020Game-Theoretic Multiagent Reinforcement Learningcited by 94
  2. 2019Provably Efficient Q-Learning with Low Switching Costcited by 78
  3. 2020Deep Model-Based Reinforcement Learning for High-Dimensional Problems, a Surveycited by 6
  4. 2016A Sensorimotor Reinforcement Learning Framework for Physical Human-Robot Interactioncited by 5

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