1 citations · 1 across the 12 of their papers we have counts for
12 papers
Let the Dynamics Flow: Stable Flow Matching Dynamical Systems
Rodrigo Pérez-Dattari, Francisco Leiva, Andrea Testa +3
Flow matching has recently emerged as a powerful approach for imitation learning, enabling scalable, expressive, and multimodal motion policies. However, when modeling these polici…
Contact Wasserstein Geodesics for Non-Conservative Schrödinger Bridges
Andrea Testa, Søren Hauberg, Tamim Asfour +1
The Schrödinger Bridge provides a principled framework for modeling stochastic processes between distributions; however, existing methods are limited by energy-conservation assumpt…
Taxonomy-aware Dynamic Motion Generation on Hyperbolic Manifolds
Luis Augenstein, Noémie Jaquier, Tamim Asfour +1
Human-like motion generation for robots often draws inspiration from biomechanical studies, which often categorize complex human motions into hierarchical taxonomies. While these t…
Towards Safe Imitation Learning via Potential Field-Guided Flow Matching
Haoran Ding, Anqing Duan, Zezhou Sun +4
Deep generative models, particularly diffusion and flow matching models, have recently shown remarkable potential in learning complex policies through imitation learning. However,…
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…
Diffeomorphic Obstacle Avoidance for Contractive Dynamical Systems via Implicit Representations
Ken-Joel Simmoteit, Philipp Schillinger, Leonel Rozo
Ensuring safety and robustness of robot skills is becoming crucial as robots are required to perform increasingly complex and dynamic tasks. The former is essential when performing…
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…
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…