Evolutionary Robotics Controllers with Proprioception Facilitated by Neural-Network-Based Simulators

Abstract

Taking inspiration from the navigation ability of humans, this study investigated a method of providing robotic controllers with a basic sense of position. It incorporated robotic simulators into robotic controllers to provide them with a mechanism to approximate the effects their actions had on the robot. Controllers with and without internal simulators were tested and compared. The proposed controller architecture was shown to outperform the regular controller architecture. However, the longer an internal simulator was executed, the more inaccurate it became. Thus, the performance of controllers with internal simulators reduced over time unless their internal simulator was periodically corrected.