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