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Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes


Conference Paper


The identification of the constrained dynamics of mechanical systems is often challenging. Learning methods promise to ease an analytical analysis, but require considerable amounts of data for training. We propose to combine insights from analytical mechanics with Gaussian process regression to improve the model's data efficiency and constraint integrity. The result is a Gaussian process model that incorporates a priori constraint knowledge such that its predictions adhere to Gauss' principle of least constraint. In return, predictions of the system's acceleration naturally respect potentially non-ideal (non-)holonomic equality constraints. As corollary results, our model enables to infer the acceleration of the unconstrained system from data of the constrained system and enables knowledge transfer between differing constraint configurations.

Author(s): A. René Geist and Sebastian Trimpe
Year: 2020
Month: June

Department(s): Intelligent Control Systems
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Event Name: 2nd Annual Conference on Learning for Dynamics and Control
Event Place: Berkeley, California, USA

State: Accepted

Links: Arxiv preprint


@inproceedings{Gaussian Processes, System identification, Gauss principle,
  title = {Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes},
  author = {Geist, A. René and Trimpe, Sebastian},
  month = jun,
  year = {2020},
  month_numeric = {6}