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.

翻译：痉挛是脑瘫、遗传性痉挛性截瘫、脊髓损伤和中风患者常见的运动障碍症状，也是这些疾病进展中最具致残性的特征之一。尽管使用可穿戴机器人治疗痉挛具有潜在益处，但目前不建议将改良Ashworth量表评级高于${1^+}$的痉挛患者纳入适用范围。这种速度依赖性强直牵张反射的动态变化特性使得部署安全的个性化控制器面临挑战。本文提出一种基于深度强化学习（RL）的膝关节外骨骼自适应扭矩控制器，该控制器在关节痉挛条件下兼顾任务执行性能与交互力降低。为训练强化学习智能体，我们开发了包含关节错位肌肉骨骼-外骨骼系统的数字孪生模型，并采用可微分痉挛反射模型进行肌肉激活模拟。膝关节伸展运动的仿真结果表明，该智能体能够学习控制外骨骼以适应不同痉挛等级的患者。与传统柔顺控制器相比，所提出的控制器在痉挛条件下能将施加于人体关节的最大扭矩平均降低10.6%，并将稳定时间前的均方根误差降低8.9%。