Experimental Robot Inverse Dynamics Identification Using Classical and Machine Learning Techniques
Vinzenz Bargsten, José de Gea Fernández, Yohannes Kassahun
In International Symposium on Robotics, (ISR), 21.6.-22.6.2016, München, o.A., 2016.

Abstract :

This paper shows the experimental identification of the inverse dynamics model of a KUKA iiwa lightweight robot. We use experimental data from optimal identification experiments to evaluate and compare two different identification approaches: a classical method using a parametrized robot dynamical model and a machine learning method. Both methods accurately estimate the dynamics model and this paper will discuss the pros and cons of each method.



last updated 28.02.2023