On-line Model Learning for Mobile Manipulations

Abstract

In control system design, modeling the control objects is the foundation of high performance controllers. In most cases, the model is unknown before the task is performed. To learn the model, therefore, is essential. Generally, real-time systems use the sensors to measure the environment and the objects. The model learning task is particularly difficult when real-time control systems run in a noisy and changing environment. The measurements from the sensors may be contaminated by the non-stationary noise, i.e. it is changing randomly and depends on the environmental uncertainties. The factors, which cause the environmental uncertainties, may include wind, vibration, friction, and so on.