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

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.