Aiming at the difficulties of high-performance robot motion control caused by the challenges such as rapid change of robot tasks and compact working space in man-machine integration scenarios, a robot motion control and real-time configuration optimization method based on environment attraction domain was proposed. In order to reduce the moving area of the robot body as much as possible, two sets of key points were used to characterize the robot body and the robot operating area preferred by humans, and an evaluation index of the environment attraction area based on the distance of the point set was proposed. The Cartesian space motion controller was designed, and the joint angular velocity equality constraint of the end effector tracking error convergence was deduced. Combined with physical constraints such as joint angle and angular velocity, a constrained optimization model for robot motion control and configuration optimization was constructed. A recurrent neural network was designed to solve the angular velocity instruction of the robot in real time, and the stability of the system was proved. Finally, the simulation results on 7-DOF Franka Panda robot show that the proposed algorithm can ensure the convergence of the end-effector’s tracking error, and shrinking the robot’s working area as much as possible to the pre-defined area.