Learning robot dynamics with Kinematic Bézier Maps

The previously presented Kinematic Bezier 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.