Normalization of inputs and outputs of neural network based robotic arm controller in role of inverse kinematic model

Goal of this paper is to discuss the methods usable to normalize inputs and outputs of the neural network controller used to control the arm of the humanoid robot with 3 degrees of freedom. The task of the controller is to solve the inverse kinematic problem, i.e. to move the hand of the humanoid robot to the target location given in arbitrary coordinate system other than its own kinematic chain defined by joint angle vector. In order to train accurate model for the controller it is necessary to normalize the values of input and output data in the training dataset. Data normalization within certain criteria, prior to the training process, is crucial to obtain satisfactory results as well as to fasten the training process itself. To proceed with the normalization we need to reduce domains of the training data in advance. Despite this task may look trivial, especially if I/O domains are clearly given, in some applications, such as finding the solution to the inverse kinematics problem of the humanoid robotic arm, it may become more complex and challenging. In this paper we will analyze possible options to perform normalization using expert oriented, automatic and hybrid approaches.