Open Access

Thomas Granser, Maximilian Gießler, Stefan Glaser, Bernd Waltersberger, Stefan Hensel

• We propose a machine-learning-based approach for identifying inertia parameters of robotic systems. We evaluate the method in simulation and compare it against classical methods. Specifically, we implement parameter identification based on numerical optimization and test it using ground truth data. For a case study, we set up a physical simulation of a four-degree-of-freedom robot arm, formulatingWe propose a machine-learning-based approach for identifying inertia parameters of robotic systems. We evaluate the method in simulation and compare it against classical methods. Specifically, we implement parameter identification based on numerical optimization and test it using ground truth data. For a case study, we set up a physical simulation of a four-degree-of-freedom robot arm, formulating the problem with Newton-Euler equations as opposed to the conventional Lagrangian formulation at the joint level. Additionally, we derive a test methodology for assessing various Artificial Neural Network architectures.…