4 papers
Reduced-order Control and Geometric Structure of Learned Lagrangian Latent Dynamics
Katharina Friedl, Noémie Jaquier, Seungyeon Kim +2
Model-based controllers can offer strong guarantees on stability and convergence by relying on physically accurate dynamic models. However, these are rarely available for high-dime…
Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach
Katharina Friedl, Noémie Jaquier, Alyx Liao +1
Embedding physical intuition into network architectures allows the learning of dynamics that enforce fundamental properties, such as energy conservation laws, thereby leading to ph…
Pushing Everything Everywhere All At Once: Probabilistic Prehensile Pushing
Patrizio Perugini, Jens Lundell, Katharina Friedl +1
We address prehensile pushing, the problem of manipulating a grasped object by pushing against the environment. Our solution is an efficient nonlinear trajectory optimization probl…
A Riemannian Framework for Learning Reduced-order Lagrangian Dynamics
Katharina Friedl, Noémie Jaquier, Jens Lundell +2
By incorporating physical consistency as inductive bias, deep neural networks display increased generalization capabilities and data efficiency in learning nonlinear dynamic models…