Experimental Robot Inverse Dynamics Identification Using Classical and Machine Learning Techniques

Conference: ISR 2016 - 47st International Symposium on Robotics
06/21/2016 - 06/22/2016 at München, Germany

Proceedings: ISR 2016

Pages: 6Language: englishTyp: PDF

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Bargsten, Vinzenz (Robotics Research Group, University of Bremen, Germany)
Gea Fernandez, Jose de; Kassahun, Yohannes (Robotics Innovation Center, DFKI, Germany)

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.