Gait synthesis of a biped robot using backpropagation through time algorithm

Abstract

A neural network architecture is developed for the gait synthesis of a five-link biped walking robot. The learning scheme uses a multilayered feedforward neural network combined with a linearized inverse biped model. It can generate walking gait by giving reference trajectory which defines a desired gait in several stages. The algorithm used to train network is known as back-propagation with time-delay or so-called backpropagation through time. A three-layered neural network is used as a controller, it provides the control signals in each stage of a walking gait. The linearized inverse biped model calculates the error signals which will be used to back propagate through the controller in each stage.