Motion Generation of Humanoid Robot based on Polynomials Generated by Recurrent Neural Network

Abstract

Abstract: Humanoid robots are expected to have variety of motions that enables good interaction with real human environment. Making a program for generating several stable motions using the standard programming language such as C is not only time consuming but also hard to understand and tune. For this, a suitable recurrent neural network language (RNN) inspired from neurobiology has been developed. In this paper, a simple method of motion generation based on polynomials generated by RNN is presented. All motions are generated using a basic RNN circuit of a first order polynomial. Using this method it is easy to generate a complex motion of humanoid robot. Furthermore, Feedback controllers can be easily inserted in the RNN circuit of a motion at any desired timing. Both rhythmic and non-rhythmic motion can be generated based on the same strategy. The effectiveness of the proposed method is verified by experimental results.