4 papers
Geometric Contact Flows: Contactomorphisms for Dynamics and Control
Andrea Testa, Søren Hauberg, Tamim Asfour +1
Accurately modeling and predicting complex dynamical systems, particularly those involving force exchange and dissipation, is crucial for applications ranging from fluid dynamics t…
Connecting Neural Models Latent Geometries with Relative Geodesic Representations
Hanlin Yu, Berfin Inal, Georgios Arvanitidis +3
Neural models learn representations of high-dimensional data on low-dimensional manifolds. Multiple factors, including stochasticities in the training process, model architectures,…
Riemann: Learning Riemannian Submanifolds from Riemannian Data
Leonel Rozo, Miguel González-Duque, Noémie Jaquier +1
Latent variable models are powerful tools for learning low-dimensional manifolds from high-dimensional data. However, when dealing with constrained data such as unit-norm vectors o…
Extended Neural Contractive Dynamical Systems: On Multiple Tasks and Riemannian Safety Regions
Hadi Beik Mohammadi, Søren Hauberg, Georgios Arvanitidis +2
Stability guarantees are crucial when ensuring that a fully autonomous robot does not take undesirable or potentially harmful actions. We recently proposed the Neural Contractive D…