Dynamic balance of a bipedal robot using neural network training with simulated annealing

This work proposes using an evolutionary optimization method known as simulated annealing to train artificial neural networks. These neural networks are used to control posture stabilization of a humanoid robot in a simulation. A total of eight multilayer perceptron neural networks are used. Although the control is used mainly for posture stabilization and not displacement, we propose a posture set to achieve this, including right leg lift in sagittal plane and right leg lift in frontal plane. At the beginning, tests are carried out only considering gravitational force and reaction force between the floor and the humanoid; then tests are carried out with two disturbances: tilted ground and adding a mass to the humanoid. We found that using simulated annealing the robot maintains its stability at all times, decreasing the number of epochs needed to converge, and also, showing flexibility and adaptability to disturbances. The way neural networks learn is analyzed; videos of the movements made, and the model for further experimentation are provided.