23 citations · 38 across the 14 of their papers we have counts for
17 papers
Compositional Sensitivity Analysis for Multiobjective System Co-Design
Alan Bonomi, Antonio Terpin, Yubo Cai +3
Sensitivity analysis of coupled systems typically propagates derivatives of single-valued maps, whereas multiobjective optimization generally treats sensitivity at the system level…
Motion planning for hundreds of floating robots
Jan Kamm, Antonio Terpin, Raffaello D'Andrea +1
Planning collision-free motion for large robot fleets is difficult because collision avoidance induces strong inter-agent coupling that grows rapidly with team size. We consider om…
Stabilizing Fast Dynamical Systems Using Time-of-Flight Cameras
Anthony Czubarow, Antonio Terpin, Raffaello D'Andrea
Time-of-flight cameras are popular in robotics because they provide direct depth information and are compact, inexpensive, and less dependent on ambient visible light. However, the…
Privileged observations enable rapid and reliable policy discovery directly in the physical world
Antonio Terpin, Raffaello D'Andrea
We study how privileged information about a physical system affects the discovery of high-performing policies when training a reinforcement learning agent directly in the physical…
Flow Gym: A framework for the development, benchmarking, training, and deployment of flow-field quantification methods
Francesco Banelli, Antonio Terpin, Alan Bonomi +1
Particle image velocimetry (PIV) and related optical-flow methods are widely used to quantify fluid motion, but their development and evaluation are often hindered by fragmented so…
Particle Image Velocimetry Refinement via Consensus ADMM for Active Fluid Control
Alan Bonomi, Francesco Banelli, Antonio Terpin
Particle Image Velocimetry (PIV) is among the central modalities for measuring flow fields across laboratory, industrial and environmental setting. Traditional PIV approaches typic…
SynthPix: A lightspeed PIV image generator
Antonio Terpin, Alan Bonomi, Francesco Banelli +1
We describe SynthPix, a synthetic image generator for Particle Image Velocimetry (PIV) with a focus on performance and parallelism on accelerators, implemented in JAX. SynthPix pro…
Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers
Panagiotis D. Grontas, Antonio Terpin, Efe C. Balta +2
We introduce an output layer for neural networks that ensures satisfaction of convex constraints. Our approach, net, leverages operator splitting for rapid and reliable projecti…
Gradient-flow SDEs have unique transient population dynamics
Vincent Guan, Joseph Janssen, Nicolas Lanzetti +3
Identifying the drift and diffusion of an SDE from its population dynamics is a notoriously challenging task. Researchers in machine learning and single-cell biology have only been…
Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling
Michal Balcerak, Tamaz Amiranashvili, Antonio Terpin +5
Current state-of-the-art generative models map noise to data distributions by matching flows or scores. A key limitation of these models is their inability to readily integrate ava…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
Distributionally Robust Linear Quadratic Gaussian Regulator with Stationary Distributions
Alain Schöbi, Nicolas Lanzetti, Florian Dörfler +2
We study the Linear Quadratic Gaussian regulation problem in the face of worst-case noise distributions when these are mutually independent, zero-mean, stationary, and within a rad…
Learning diffusion at lightspeed
Antonio Terpin, Nicolas Lanzetti, Martin Gadea +1
Diffusion regulates numerous natural processes and the dynamics of many successful generative models. Existing models to learn the diffusion terms from observational data rely on c…
Variational Analysis in the Wasserstein Space
Nicolas Lanzetti, Antonio Terpin, Florian Dörfler
We study optimization problems whereby the optimization variable is a probability measure. Since the space of probability measures is not a vector space, many classical methods for…
Dynamic Programming in Probability Spaces via Optimal Transport
Antonio Terpin, Nicolas Lanzetti, Florian Dörfler
We study discrete-time finite-horizon optimal control problems in probability spaces, whereby the state of the system is a probability measure. We show that, in many instances, the…
Trust Region Policy Optimization with Optimal Transport Discrepancies: Duality and Algorithm for Continuous Actions
Antonio Terpin, Nicolas Lanzetti, Batuhan Yardim +2
Policy Optimization (PO) algorithms have been proven particularly suited to handle the high-dimensionality of real-world continuous control tasks. In this context, Trust Region Pol…
Distributed Feedback Optimisation for Robotic Coordination
Antonio Terpin, Sylvain Fricker, Michel Perez +2
Feedback optimisation is an emerging technique aiming at steering a system to an optimal steady state for a given objective function. We show that it is possible to employ this con…