Learning robot dynamics with Kinematic Bézier Maps

The previously presented Kinematic Bézier Maps (KBM) are a machine learning algorithm that has been tailored to efficiently learn the kinematics of redundant robots. This algorithm relies upon a representation based on projective geometry that uses a special set of polynomial functions borrowed from the field of Computer Aided Geometric Design (CAGD). So far, it has only been possible to learn a model of the forward kinematics function. In this paper, we show how the KBM algorithm can be modified to learn the robot´s equation of motion and, hence, its inverse dynamic model. Results from experiments with a simulated serial robot manipulator are presented that clearly show the advantages of our approach compared to general function approximation methods.