Reinforcement Learning-Based Grasp Pattern Control of Upper Limb Prosthetics in an AI Platform

Many recent improvements have been made in controlling and simulating prosthetics. Various hand-grasping poses result from almost 20 degrees of freedom in the human hand. A rich mechanical structure for multigrasp is needed to provide activities in prostheses for individuals with limb loss. Even though researchers have used novel techniques to simulate musculoskeletal models of prostheses in different situations, one of the critical issues and challenges in this field is the lack of a comprehensive simulation environment to simulate realistic upper limbs. In this study, we used a complex and rich environment to train a musculoskeletal hand model with 29 bones, 23 joints, and 39 muscle-tendon units to control reaching a fixed pose and grasping a ball in the virtual environment. Also, in the training process, we applied the deep reinforcement learning procedure to this model. Finally, we performed a simulation in a virtual environment of MyoSuite to demonstrate the efficiency of the proposed deep reinforcement learning algorithm. The result carried out the accurate orientation and grasping of the hand to hold the ball.