Initial weight estimation for learning the internal model based on the knowledge of the robot morphology

Abstract

The information used to determine the internal model of a robot system emerges from individual interactions with the environment. Knowledge about a specific internal model can be acquired by means of model learning techniques based on supervised machine learning tools, such as neural networks. One of the main challenges is to specify the starting point for the learning process. We propose a methodology for initial weight estimation based on the robot morphology. We use the generation of saccades in a robotic head as a case study to evaluate the performance of this approach. Our results suggest that this methodology, based on learning the relationship between the morphological parameters of the robot and those in its internal model, improves the quality of weight initialization, resulting in a considerable speed-up in the process of learning the internal model.