Learning and Transfer Methods for Active Rehabilitation Strategy of Upper-limb Rehabilitation Robot

To achieve active upper-limb rehabilitation for stroke patients, a feedback control strategy based on reinforcement learning is proposed, to transfer the control strategy to the physical environment after training. Firstly, human upper-limb movement is analyzed to establish an equivalent model, and a random trajectory planning method is proposed to mimic voluntary movement in joint space. Subsequently, a simulation environment for man-robot system is established based on the equivalent model. To train the control strategy, the complete states of the system are mapped to observable states through kinematic analysis on the human body and robot. Finally, a feedback control strategy is used to align the feature distribution between environments, in order to transfer the trained control strategy to the robot and avoid negative transfer. Simulation results demonstrate the effectiveness of the reinforcement learning-based control strategy in accomplishing active rehabilitation tasks for stroke patients. The control strategy trained in simulation is transferred to the real robot, and the experimental results verify the effectiveness of the learning and transfer method of rehabilitation strategy, which provides a new technical solution for the active rehabilitation of movement disorder patients.