Adaptive Torque Control of Exoskeletons Under Spasticity Conditions via Reinforcement Learning

• Published in: IEEE International Conference on Rehabilitation Robotics Vol. 2025; pp. 705 - 711
• Main Authors: Chavarrias, Andres, Rodriguez-Cianca, David, Lanillos, Pablo
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.05.2025
• Subjects: Deep Learning; Diseases; Dynamics; Exoskeleton Device; Exoskeletons; Humans; Knee Joint - physiopathology; Movement disorders; Muscle Spasticity - physiopathology; Muscle Spasticity - rehabilitation; Muscles; Musculoskeletal systems; Reinforcement Learning; Reinforcement, Psychology; Root mean square; Spasticity; Spinal cord injury; Torque; Torque control; Wearable Robotics; Wearable robots
• ISSN: 1945-7901; 1945-7901
• DOI: 10.1109/ICORR66766.2025.11063182

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^{+} in the Modified Ashworth Scale. The varying dynamics of this velocity-dependent tonic stretch reflexes makes 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 musculoskeletalexoskeleton 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 \mathbf{1 0. 6 \%} and decreases the root mean square until the settling time by 8.9 % compared to a conventional compliant controller.