Self-learning control of cooperative motion for a humanoid robot

This paper deals with the problem of self-learning cooperative motion control for a heavy work of a humanoid robot in the sagittal plane. A model with 27 linked rigid bodies is developed to simulate the system dynamics. A simple genetic algorithm (SGA) is used to find the necessary torques in each joint to obtain a desired cooperative motion, which is to minimize the total energy consumption, for the humanoid robot´s postures of trunk and hands. And the multilayer neural network using the backpropagation is also described in order to control the system in real time