Bipedal Gait Adaptation For Walking With Dynamic Balance

Abstract

This paper presents preliminary results of a study of the application of CMAC neural networks to the problem of biped walking with dynamic balance. A simple fixed control strategy has been developed, requiring no a priori dynamic model, which is then augmented using neural network learning. Standard supervised learning, temporal difference learning, and reinforcement learning are combined to train the neural network. Results of simulation studies using a simple two-dimensional simulation are presented. Random training using frequent sudden changes in desired velocity produced a robust controller able to track sudden changes in the desired velocity command, and able to rapidly adjust to unexpected disturbances.