Abstract
Forward dynamics models are the important basis of motion planning of robots. While recent advancement in Robotics has allowed diverse morphologies of robots to take place, there has been little study on identifying the forward models for such robots. In this paper, we propose to use neuroevolution algorithms, which optimize the parameters of neural networks, to learn such models. We show that a neuroevolution algorithm called CMA-NeuroES can learn models of a simulated double pendulum system with higher accuracy and generalization ability compared to conventional regression algorithms, such as Gaussian Process Regression, Support Vector Regression and Linear Regression.
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