Modeling human strategy in controlling a dynamically stabilized robot

Abstract

We present a method to model human operator's strategy in controlling a dynamically stabilized robot, Gyrover, which is a single-wheel gyroscopically stabilized robot. We first select the relevant state variables for training from kinematic and dynamic equations. Then, we defined a measure of the sensitivity of each of the state variables with respect to operator's control input by a sensitivity function in order to reduce the number of the state variables required in the model. We experimentally implemented the method and demonstrated that the robot can be automatically controlled using the learned human control model. The work is of significance in abstracting operator's skill for controlling a dynamically stabilized system in generating an automatic control input.