1 citations · 1 across the 12 of their papers we have counts for
5 papers · 1 filter
Fast and Robust Visuomotor Riemannian Flow Matching Policy
Haoran Ding, Noémie Jaquier, Jan Peters +1
Diffusion-based visuomotor policies excel at learning complex robotic tasks by effectively combining visual data with high-dimensional, multi-modal action distributions. However, d…
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…
On Probabilistic Pullback Metrics for Latent Hyperbolic Manifolds
Luis Augenstein, Noémie Jaquier, Tamim Asfour +1
Probabilistic Latent Variable Models (LVMs) excel at modeling complex, high-dimensional data through lower-dimensional representations. Recent advances show that equipping these la…
The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs
Peter Mostowsky, Vincent Dutordoir, Iskander Azangulov +6
Kernels are a fundamental technical primitive in machine learning. In recent years, kernel-based methods such as Gaussian processes are becoming increasingly important in applicati…
Neural Contractive Dynamical Systems
Hadi Beik-Mohammadi, Søren Hauberg, Georgios Arvanitidis +3
Stability guarantees are crucial when ensuring a fully autonomous robot does not take undesirable or potentially harmful actions. Unfortunately, global stability guarantees are har…