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…
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…
Symmetries Here and There, Combined Everywhere: Cross-space Symmetry Compositions in Robotics
Loizos Hadjiloizou, Rodrigo Pérez-Dattari, Noémie Jaquier
Robots exhibit a rich variety of symmetries arising from their mechanical structure and the properties of their tasks. Although many robotics problems exhibit several symmetries si…
Information Theoretic Bayesian Optimization over the Probability Simplex
Federico Pavesi, Antonio Candelieri, Noémie Jaquier
Bayesian optimization is a data-efficient technique that has been shown to be extremely powerful to optimize expensive, black-box, and possibly noisy objective functions. Many appl…
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…
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…