A humanoid control strategy based on deep reinforcement learning for enhanced comfort in lower limb rehabilitation robots

The development of humanoid intelligent controllers represents a breakthrough in enhancing the effectiveness and comfort of rehabilitation training using lower limb rehabilitation robots for diverse gait patterns. In this study, a kinematic model of the lower limb rehabilitation robot is established based on a simplified link structure. For dynamic modeling, the Lagrange formulation is employed to analyze human lower limb motion from an energy-based perspective.Gait data were collected from five healthy subjects, with each performing 20 walking trials, yielding a total of 100 gait cycles for analysis. The acquired gait data are filtered and fitted to plan a reference trajectory of anthropomorphic joint angles suitable for rehabilitation training. Joint torques are computed from plantar force data and the dynamic model, serving as feedback for the controller and used in comfort assessment. A humanoid control strategy integrating Deep Reinforcement Learning (DRL) and a Proportional-Differential (PD) controller is proposed. This approach facilitates individualized gait trajectory planning for rehabilitation training by learning human gait characteristics. Simulation results demonstrate that the proposed method not only improves the intelligence and human-likeness of the robot but also significantly enhances comfort during training.