用强化学习让外骨骼自适应调节扭矩，减轻痉挛患者的关节负担。
Adaptive Torque Control of Exoskeletons under Spasticity Conditions via Reinforcement Learning
- 基于深度强化学习构建自适应扭矩控制器，动态应对痉挛引起的肌张力变化。
- 仿真结果显示，最大关节扭矩平均降低10.6%，系统响应更平稳，快8.9%收敛。
- 适合康复机器人研发者、神经康复临床研究者参考，尤其关注痉挛患者应用。
痉挛是脑性瘫痪、遗传性痉挛截瘫、脊髓损伤和中风等运动障碍的常见症状，常导致严重功能受限。尽管可穿戴机器人有望用于治疗痉挛，但目前不建议在改良Ashworth量表评分高于1+的患者中使用。由于痉挛依赖速度的肌张力反射特性复杂，难以设计安全个性化的控制策略。本文提出一种基于深度强化学习的膝外骨骼自适应扭矩控制器，兼顾任务表现与人机交互力的降低。为训练智能体，我们构建了一个数字孪生系统，包含带关节错位的肌肉骨骼-外骨骼模型及可微分的痉挛反射模型。模拟膝伸展运动结果表明，该控制器能有效适应不同痉挛程度的个体。相比传统柔性控制器，其在痉挛条件下平均降低最大关节扭矩10.6%，并使均方根误差在稳定时间内减少8.9%。
原文摘要 · Abstract (English)
Spasticity is a common movement disorder symptom in individuals with cerebral palsy, hereditary spastic paraplegia, spinal cord injury and stroke, being one of the most disabling features in the progression of these diseases. Despite the potential benefit of using wearable robots to treat spasticity, their use is not currently recommended to subjects with a level of spasticity above ${1^+}$ on the Modified Ashworth Scale. The varying dynamics of this velocity-dependent tonic stretch reflex make it difficult to deploy safe personalized controllers. Here, we describe a novel adaptive torque controller via deep reinforcement learning (RL) for a knee exoskeleton under joint spasticity conditions, which accounts for task performance and interaction forces reduction. To train the RL agent, we developed a digital twin, including a musculoskeletal-exoskeleton system with joint misalignment and a differentiable spastic reflexes model for the muscles activation. Results for a simulated knee extension movement showed that the agent learns to control the exoskeleton for individuals with different levels of spasticity. The proposed controller was able to reduce maximum torques applied to the human joint under spastic conditions by an average of 10.6\% and decreases the root mean square until the settling time by 8.9\% compared to a conventional compliant controller.
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