On-line Dynamic Model Learning for Manipulator Control

Abstract This paper proposes an approach for online learning of the dynamic model of a robot manipulator. The dynamic model is formulated as a weighted sum of locally linear models, and Locally Weighted Projection Regression (LWPR) is used to learn the models based on training data obtained during operation. The LWPR model can be initialized with partial knowledge of rigid body parameters to improve the initial performance. The resulting dynamic model is used to implement a model-based controller. Both feedforward and feedback configurations are investigated. The proposed approach is tested on an industrial robot, and shown to outperform independent joint and fixed model-based control.