Recurrent neural network learning with an application to the control of legged locomotion

Abstract

and their benefits are discussed.Finally leg angles of walking biped robot are taught to a group of RNNs with different configurations by benefiting from training stability enhancing methods.The resulting RNNs are then used in biped locomotion by using a classical PD controller.After that, performance of resulting RNNs and their stable locomotion generation capabilities are evaluated and effects of configuration parameters are discussed in detail.