Human-robot interactive control based on reinforcement learning for gait rehabilitation training robot

A human-robot interactive control is proposed to govern the assistance provided by a lower limb exoskeleton robot to patients in the gait rehabilitation training. The rehabilitation training robot with two lower limb exoskeletons is driven by the pneumatic proportional servo system and has two rotational degrees of freedom of each lower limb. An adaptive admittance model is adopted considering its suitability for human-robot interaction. The adaptive law of the admittance parameters is designed with Sigmoid function and the reinforcement learning algorithm. Individualized admittance parameters suitable for patients are obtained by reinforcement learning. Experiments in passive and active rehabilitation training modes were carried out to verify the proposed control method. The passive rehabilitation training experimental results verify the effectiveness of the inner-loop position control strategy, which can meet the demands of gait tracking accuracy in rehabilitation training. The active rehabilitation training experimental results demonstrate that the personal adaption and active compliance are provided by the interactive controller in the robot-assistance for patients. The combined effects of flexibility of pneumatic actuators and compliance provided by the controller contribute to the training comfort, safety, and therapeutic outcome in the gait rehabilitation.