Components for a robot control system

 The main problem in robotics is, that it is hard to define some recipes which are working well for all domains. Suppose the idea is to contruct a robot which can drive on a motorcyle, or a robot forklift which can load a cargo. What is the basic principle to control these different kind of robots?

A possible walk through to solve these difficult domains is a combination of voice control command processing, model predictive control and learned cost function. Let us go into the details. The idea behind voice control is, that the robot is controlled manual but not with a joystick but with natural language. For example, the human operator can say “robot start”, or “robot load the cargo”. This sort of interaction is important because it allows to show the entire picture of a robot domain which includes the actions not automated yet. An interaction between human operator and robot is needed, if certain parts of the control system are missing. In such a case the robot is controlled with teleoperation.

The second element of a robot control system is the mentioned model predictive control tool. MPC means to predict future system states and determine the optimal action. The last strategy on the list is a cost function. A cost function helps to guide the search in the problem space. Learning a cost function is equal to inverse reinforcement learning, which is sometimes called learning from demonstration . The idea is that during the demonstration the parameters are found which are defining which sort of behavior is wanted.