Two-Legged Robot Motion Control With Recurrent Neural Networks

Abstract

Legged locomotion is a desirable ability for robotic systems thanks to its agile mobility and wide range of motions that it provides. In this paper, the use of neural network-based nonlinear controller structures which consist of recurrent and feedforward layers have been examined in the dynamically stable walking problem of two-legged robots. In detail, hybrid neural controllers, in which long short-term memory type of neuron models employed at recurrent layers, are utilized in the feedback and feedforward paths. To train these neural networks, supervised learning data sets are created by using a biped robot platform which is controlled by a central pattern generator. Then, the ability of the neural networks to perform stable gait by controlling the robot platform is examined under various ground conditions in the simulation environment. After that, the stable walking generation capacity of the neural networks and the central pattern generators are compared with each other. It is shown that the inclusion of recurrent layer provides smooth transition and control between stance and flight motion phases and L 2 regularization is beneficial for walking performance. Finally, the proposed hybrid neural network models are found to be more successful gait controllers than the central pattern generator, which is employed to generate data sets used in training.

An overview on bipedal gait control methods

“…The experiments present the fast learning speed and high identification accuracy of this method. Çatalbaş [89] presented the NN-based system identification algorithms for the biped robot via the parallel and series-parallel schemes, which apply both feedforward layer and recurrent layer to reduce the error rates. Sun et al [90] designed the NN-based bipedal control strategies via radial basis function, which aims to cope with system uncertainties.…”