A study on the efficiency of learning a robot controller in various environments

Abstract

In the case that a robot controller is trained by means of evolutionary computation, the robot will be able to behave sufficiently in the environment where the robot has been trained. However, if the robot is put in an environment which is more complex than a training environment, it cannot behave sufficiently and is required to be trained again so as it fits to the complex environment. Based on this fact, we build a training environment for a robot controller with the partial components of a more complex environment than the training environment and aim to obtain a controller which makes a robot be able to act in the complex environment by only training the controller at a simpler environment. We clarify a way of building a training environment which functions effectively for training a robot controller and discuss how much training is necessary in the training environment for a robot to be able to behave under a more complex environment.