Motion generation of a biped locomotive robot using an inverted pendulum model and neural networks

The authors introduce a hierarchical structure for motion planning and learning control of a biped locomotive robot. The motion of the center of gravity of the robot is simulated by that of an inverted pendulum. A Hopfield-type neural network is used for solving the inverse kinematics in order to obtain joint positions from the position of the center of gravity and the position of the toes calculated from the equation of an inverted pendulum. A feedforward input, generated by a three-layered neural network, is used as a correcting reference input to make the motion of the center of gravity follow that of the inverted pendulum. Simulation results showed that stationary walking was successfully achieved. The proposed method thus provides an autonomous motion generation where only the position and velocity of the center of gravity of the robot for each step are given a priori