Publikation
Floating-Base Deep Lagrangian Networks
Lucas Schulze; Juliano Negri; Victor Barasuol; Vivian Suzano Medeiros; Marcelo Becker; Jan Peters; Oleg Arenz
In: IEEE International Conference on Robotics and Automation, ICRA 2026, Vienna, Austria, June 1-5, 2026. IEEE International Conference on Robotics and Automation (ICRA), Pages 18554-18561, IEEE, 2026.
Zusammenfassung
Grey-box methods for system identification com-
bine deep learning with physics-informed constraints, captur-
ing complex dependencies while improving out-of-distribution
generalization. Despite the growing importance of floating-base
systems such as humanoids and quadrupeds, current grey-box
models ignore their specific physical constraints. For instance,
the inertia matrix is not only positive definite but also exhibits
branch-induced sparsity and input independence. Moreover,
the 6×6 composite spatial inertia of the floating base inherits
properties of single-rigid-body inertia matrices. As we show,
this includes the triangle inequality on the eigenvalues of the
composite rotational inertia. To address the lack of physical
consistency in deep learning models of floating-base systems, we
introduce a parameterization of inertia matrices that satisfies
all these constraints. Inspired by Deep Lagrangian Networks
(DeLaN), we train neural networks to predict physically plau-
sible inertia matrices that minimize inverse dynamics error
under Lagrangian mechanics. For evaluation, we collected and
released a dataset on multiple quadrupeds and humanoids.
In these experiments, our Floating-Base Deep Lagrangian
Networks (FeLaN) achieve better overall performance on both
simulated and real robots, while providing greater physical
interpretability.