Before a motion controller for a robot can be created, a step before is needed, called “system identification”. The aim is to create a prediction model which can say, what will happen if a certain movement of the robot is executed. For example, a wheeled robot which is designed upon the steering principle will change it's position if both wheels are spin with +1 speed for 1 second. The new x/y position will be ahead of the old position. If the robot's wheel are spinning with different values, for example left=+1, right=+0.5 the new position of the robot will be different.
The problem is, that a dedicated physics engine which can predict the trajectory of a robot is hard to realize by hand. But there are some general techniques available for example genetic algorithm, rule based systems or reinforcement learning which can simplify the programming. These general systems are working with parameters who have to be adapted to a certain domain, which is called machine learning.
Question: Can a neural network be used for predicting future states of a robot? And which kind of dataset is needed for doing so?