Adaptive Torque Control of Exoskeletons under Spasticity Conditions via Reinforcement Learning
This addresses the challenge of safe personalized exoskeleton control for patients with movement disorders like cerebral palsy, though it is incremental as it builds on existing RL and control methods.
The paper tackled the problem of controlling exoskeletons for individuals with spasticity by developing an adaptive torque controller using deep reinforcement learning, which reduced maximum torques by 10.6% and root mean square until settling time by 8.9% compared to a conventional controller.
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.