Learning Control Theory and Foundations
Learning algorithms hold great promise for improving a robot’s performance whenever a-priori models are not sufficiently accurate. We have developed learning controllers of different complexity ranging from controllers that improve the execution of a specific task by iteratively updating the reference input, to task-independent schemes that update the underlying robot model whenever new data becomes available. However, all learning controllers have the following characteristics:
- they combine a-priori model information with experimental data,
- they make no major, a-priori assumptions about the unknown effects to be learned, and
- they have been tested extensively on state-of-the-art robotic platforms.