用离线强化学习自动调优外骨骼参数，减少人工校准依赖。
Investigating Adaptive Tuning of Assistive Exoskeletons Using Offline Reinforcement Learning: Challenges and Insights
- 分代理优化肱二头肌与肱三头肌阈值，实现自适应控制。
- 在两种手臂动作任务中成功动态调整阈值，提升交互体验。
- 基于预收集数据训练，避免实时探索风险，适合康复工程应用。
助行外骨骼在改善运动障碍者行动能力方面展现出巨大潜力，但其效果依赖于针对个体的精确参数调校。本研究探讨了离线强化学习在上肢助行外骨骼中优化努力阈值的可行性，旨在降低对人工校准的依赖。具体而言，将问题建模为多智能体系统，分别由独立智能体优化肱二头肌和肱三头肌的努力阈值，实现更自适应、数据驱动的控制策略。采用混合Q函数（Mixed Q-Functionals, MQF）有效处理连续动作空间，并利用预收集数据，避免实时探索带来的风险。实验使用MyoPro 2外骨骼，在水平与垂直手臂运动两类任务中进行验证。结果表明，该方法可根据学习到的模式动态调整阈值，可能提升用户交互与控制效果，但受数据集限制，性能评估仍具挑战性。
原文摘要 · Abstract (English)
Assistive exoskeletons have shown great potential in enhancing mobility for individuals with motor impairments, yet their effectiveness relies on precise parameter tuning for personalized assistance. In this study, we investigate the potential of offline reinforcement learning for optimizing effort thresholds in upper-limb assistive exoskeletons, aiming to reduce reliance on manual calibration. Specifically, we frame the problem as a multi-agent system where separate agents optimize biceps and triceps effort thresholds, enabling a more adaptive and data-driven approach to exoskeleton control. Mixed Q-Functionals (MQF) is employed to efficiently handle continuous action spaces while leveraging pre-collected data, thereby mitigating the risks associated with real-time exploration. Experiments were conducted using the MyoPro 2 exoskeleton across two distinct tasks involving horizontal and vertical arm movements. Our results indicate that the proposed approach can dynamically adjust threshold values based on learned patterns, potentially improving user interaction and control, though performance evaluation remains challenging due to dataset limitations.
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