用专家数据训练机器人，自适应调整理疗指令。
Policy Learning for Social Robot-Led Physiotherapy
- 以33位医疗专家为代理，构建患者行为模型。
- 模拟训练出能适配不同耐受度和恢复阶段的策略。
- 适合需要个性化理疗引导的康复机器人研发。
社交机器人有望自主引导患者完成理疗训练，但有效应用需具备应对个体需求的智能决策能力。主要挑战在于缺乏足够的患者行为数据以训练稳健策略。为此，我们邀请33位专业医疗人员作为患者代理，通过他们与机器人的互动数据，构建了可生成运动表现指标和主观疲劳评分的患者行为模型。我们在仿真环境中训练基于强化学习的策略，证明其能根据个体疲劳耐受度和波动的运动表现动态调整指导内容，并适用于不同恢复阶段、具有差异化训练计划的患者。
原文摘要 · Abstract (English)
Social robots offer a promising solution for autonomously guiding patients through physiotherapy exercise sessions, but effective deployment requires advanced decision-making to adapt to patient needs. A key challenge is the scarcity of patient behavior data for developing robust policies. To address this, we engaged 33 expert healthcare practitioners as patient proxies, using their interactions with our robot to inform a patient behavior model capable of generating exercise performance metrics and subjective scores on perceived exertion. We trained a reinforcement learning-based policy in simulation, demonstrating that it can adapt exercise instructions to individual exertion tolerances and fluctuating performance, while also being applicable to patients at different recovery stages with varying exercise plans.
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