Deep Reinforcement Learning for Control of Time-Varying Musculoskeletal Systems With High Fatigability: A Feasibility Study.

Functional electrical stimulation (FES) can be used to restore motor function to people with paralysis caused by spinal cord injuries (SCIs). However, chronically-paralyzed FES-stimulated muscles can fatigue quickly, which may decrease FES controller performance. In this work, we explored the feasibility of using deep neural network (DNN) controllers trained with reinforcement learning (RL) to control FES of upper-limb muscles after SCI. We developed upper-limb biomechanical models that exhibited increased muscle fatigability, decreased muscle recovery, and decreased muscle strength, as observed in people with chronic SCIs. Simulations confirmed that controller training time and controller performance are impaired to varying degrees by muscle fatigability. Also, the simulations showed that large muscle strength asymmetries between opposing muscles can substantially impair controller performance. However, the results of this study suggest that controller performance for highly-fatigable musculoskeletal systems can be preserved by allowing for rest between movements. Overall, the results suggest that RL can be used to successfully train FES controllers, even for highly-fatigable musculoskeletal systems.

Improving the Learning Rate, Accuracy, and Workspace of Reinforcement Learning Controllers for a Musculoskeletal Model of the Human Arm.

Cervical spinal cord injuries frequently cause paralysis of all four limbs - a medical condition known as tetraplegia. Functional electrical stimulation (FES), when combined with an appropriate controller, can be used to restore motor function by electrically stimulating the neuromuscular system. Previous works have demonstrated that reinforcement learning can be used to successfully train FES controllers. Here, we demonstrate that transfer learning and curriculum learning can be used to improve the learning rates, accuracies, and workspaces of FES controllers that are trained using reinforcement learning.

Hindsight Experience Replay Improves Reinforcement Learning for Control of a MIMO Musculoskeletal Model of the Human Arm.

High-level spinal cord injuries often result in paralysis of all four limbs, leading to decreased patient independence and quality of life. Coordinated functional electrical stimulation (FES) of paralyzed muscles can be used to restore some motor function in the upper extremity. To coordinate functional movements, FES controllers should be developed to exploit the complex characteristics of human movement and produce the intended movement kinematics and/or kinetics. Here, we demonstrate the ability of a controller trained using reinforcement learning to generate desired movements of a horizontal planar musculoskeletal model of the human arm with 2 degrees of freedom and 6 actuators. The controller is given information about the kinematics of the arm, but not the internal state of the actuators. In particular, we demonstrate that a technique called "hindsight experience replay" can improve controller performance while also decreasing controller training time.