Interactive control algorithm for shoulder-amputated prosthesis and object based on reinforcement learning

As a prosthesis made to compensate for the residual loss of the amputee’s limb, the shoulder disarticulation upper limb prosthesis replaces the missing arm function of the shoulder amputee to a certain extent. However, the current upper limb prosthesis mainly interacts with the outside world through the prosthetic hand for grasping and gripping, and the interaction between other parts and the environment is often neglected, which is not in line with the use habits of the human arm. To address this problem, this paper proposes a reinforcement learning–based method for controlling the forearm interaction of a shoulder-disconnected upper limb prosthesis, and analyzes and solves the forces during the interaction, reducing the impact of uncertainty on interaction actions and accelerating training while ensuring the stability of handheld items. We evaluated the performance of the control method during the interaction between the upper limb prosthesis and the external environment through simulation experiments. After the training, the bionic arm was able to push the object into the target range for different objects and pushing distance requirements, which showed the good control effect of the method. Also, the control method can be applied to improve the interaction between the robotic arm and the environment.