31 citations · 70 across the 31 of their papers we have counts for
32 papers
ChainSplat: A Physics-Inspired Screw-Theoretic Model for Learning Deformable Linear Object Dynamics from Multi-View RGB Videos
Seungyeon Kim, Noémie Jaquier
Identifying the underlying dynamics and 3D geometry of deformable linear objects (DLOs), such as cables, ropes, and hoses, is essential for accurate robotic manipulation, but remai…
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…
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…
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…
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,…
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…
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…
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…
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…
Bi-KVIL: Keypoints-based Visual Imitation Learning of Bimanual Manipulation Tasks
Jianfeng Gao, Xiaoshu Jin, Franziska Krebs +2
Visual imitation learning has achieved impressive progress in learning unimanual manipulation tasks from a small set of visual observations, thanks to the latest advances in comput…
Riemannian Flow Matching Policy for Robot Motion Learning
Max Braun, Noémie Jaquier, Leonel Rozo +1
We introduce Riemannian Flow Matching Policies (RFMP), a novel model for learning and synthesizing robot visuomotor policies. RFMP leverages the efficient training and inference ca…
Incremental Learning of Full-Pose Via-Point Movement Primitives on Riemannian Manifolds
Tilman Daab, Noémie Jaquier, Christian Dreher +3
Movement primitives (MPs) are compact representations of robot skills that can be learned from demonstrations and combined into complex behaviors. However, merely equipping robots…
Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges
Noémie Jaquier, Michael C. Welle, Andrej Gams +6
Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied agents. The core concept -- reusing prior knowledge to learn in and from novel situat…
Unraveling the Single Tangent Space Fallacy: An Analysis and Clarification for Applying Riemannian Geometry in Robot Learning
Noémie Jaquier, Leonel Rozo, Tamim Asfour
In the realm of robotics, numerous downstream robotics tasks leverage machine learning methods for processing, modeling, or synthesizing data. Often, this data comprises variables…
On the Design of Region-Avoiding Metrics for Collision-Safe Motion Generation on Riemannian Manifolds
Holger Klein, Noémie Jaquier, Andre Meixner +1
The generation of energy-efficient and dynamic-aware robot motions that satisfy constraints such as joint limits, self-collisions, and collisions with the environment remains a cha…
Bringing motion taxonomies to continuous domains via GPLVM on hyperbolic manifolds
Noémie Jaquier, Leonel Rozo, Miguel González-Duque +2
Human motion taxonomies serve as high-level hierarchical abstractions that classify how humans move and interact with their environment. They have proven useful to analyse grasps,…
Riemannian geometry as a unifying theory for robot motion learning and control
Noémie Jaquier, Tamim Asfour
Riemannian geometry is a mathematical field which has been the cornerstone of revolutionary scientific discoveries such as the theory of general relativity. Despite early uses in r…
K-VIL: Keypoints-based Visual Imitation Learning
Jianfeng Gao, Zhi Tao, Noémie Jaquier +1
Visual imitation learning provides efficient and intuitive solutions for robotic systems to acquire novel manipulation skills. However, simultaneously learning geometric task const…
A Riemannian Take on Human Motion Analysis and Retargeting
Holger Klein, Noémie Jaquier, Andre Meixner +1
Dynamic motions of humans and robots are widely driven by posture-dependent nonlinear interactions between their degrees of freedom. However, these dynamical effects remain mostly…
Learning to Sequence and Blend Robot Skills via Differentiable Optimization
Noémie Jaquier, You Zhou, Julia Starke +1
In contrast to humans and animals who naturally execute seamless motions, learning and smoothly executing sequences of actions remains a challenge in robotics. This paper introduce…