Model-Based Robot Learning
Models play an important role in learning from practice. Models of a controlled system can be used as learning operators to refine commands on the basis of performance errors. The examples used to demonstrate this include positioning a limb at a visual target and following a defined trajectory. Better models lead to faster correction of command errors, requiring less practice to attain a given level of performance. The benefits of accurate modeling are improved performance in all aspects of control, while the risks of inadequate modeling are poor learning performance, or even degradation of performance with practice.