Recurrent neural network approaches for biped walking robot based on zero-moment point criterion

Abstract

The main objective of this paper is to use a recurrent neural network (RNN) to determine the trunk motion for a biped-walking machine, based on the zero-moment point (ZMP) criterion. ZMP criterion can be used to plan a stable gait for a biped-walking machine that has a trunk (inverted pendulum). So, a RNN is trained to determine a compensative trunk motion that makes the actual ZMP get closer to the planned ZMP. In this context, an identification scheme is presented to obtain the vector of parameters of the RNN. A first order standard back-propagation with momentum (BPM) is used to adjust free parameters for the network. Artificial neural network brings up important features for function approximation. This was the main reason to use an RNN to determine the trunk motion. The proposed scheme is simulated on a 10-degree-of-freedom biped robot. The results confirm the convergence of the proposed scheme, proving this is a new way to solve this classical problem in the biped-walking machine area.