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cs.RO2025
Zero-Shot Parameter Learning of Robot Dynamics Using Bayesian Statistics and Prior Knowledge
Carsten Reiners, Minh Trinh, Lukas Gründel +4
Inertial parameter identification of industrial robots is an established process, but standard methods using Least Squares or Machine Learning do not consider prior information abo…
cs.RO2025
Newtonian and Lagrangian Neural Networks: A Comparison Towards Efficient Inverse Dynamics Identification
Minh Trinh, Andreas René Geist, Josefine Monnet +3
Accurate inverse dynamics models are essential tools for controlling industrial robots. Recent research combines neural network regression with inverse dynamics formulations of the…