The design of motion control systems for legged robots has always been a challenge. This article first proposes a motion control method for legged robots based on the gradient central pattern generator (GD-CPG). The periodic signals output from the GD-CPG neural network are used as the drive signals of each thigh joint of the legged robots, which are then converted into the driving signal of the knee and ankle joints by the thigh–knee mapping function and the knee–ankle mapping function. The proposed control algorithm is adapted to quadruped and hexapod robots. To improve the ability of legged robots to cope with complex terrains, this article further proposes the responsive gradient-CPG motion control method for legged robots. From the perspective of bionics, a biological vestibular sensory feedback mechanism is established in the control system. The mechanism adjusts the robot’s motion state in real time through the attitude angle of the body measured during the robot’s motion, to keep the robot’s body stable when it moves in rugged terrains. Compared with the traditional feedback model that only balances the body pitch, this article also adds the balancing functions of body roll and yaw to balance the legged robot’s motion from more dimensions and improve the linear motion capability. This article also introduces a differential evolutionary algorithm and designs a fitness function to adaptively optimize vestibular sensory feedback parameters. The validity, robustness, and transferability of the method are verified through simulations and physical experiments.