Abstract: Disclosed is a dual-robot position/force multivariate-data-driven method using reinforcement learning. A master robot adopts an ideal position meta-control strategy, learns a desired position by a reinforcement learning algorithm, and feeds back an actual position to a desired position, and a goal is to generate an optimal force while the robot interacts with the environment, as to minimize a position error; and a slave robot, based on a force meta-control strategy of position deviation of the master robot, adopts a damping proportional-derivative (PD) control strategy suitable for an unknown environment, and learns a desired acting force by the reinforcement learning algorithm, namely a minimum force for driving the slave robot to approach a desired reference point. The present invention may improve the dexterity of dual-robot collaboration, solve a parameter optimization problem in position/force control.