6 papers
CaLiSym: Learning Symplectic Dynamics of Real-World Systems through Structured Canonical Lifts
Aristotelis Papatheodorou, Pranav Vaidhyanathan, Natalia Ares +2
Physics-informed learning promises data-efficient and stable dynamics prediction, yet its strongest geometric guarantees have largely remained confined to closed conservative syste…
Elastic ODYN: Differentiable Optimization for Infeasible Control and Learning in Robotics
Aristotelis Papatheodorou, Jose Rojas, Ioannis Havoutis +1
Robotic systems routinely encounter conflicting objectives, modeling errors, and degenerate contact conditions that render quadratic programs (QPs) infeasible. Yet most optimizatio…
ODYN: An All-Shifted Non-Interior-Point Method for Quadratic Programming in Robotics and AI
Jose Rojas, Aristotelis Papatheodorou, Sergi Martinez +3
We introduce ODYN, a novel all-shifted primal-dual non-interior-point quadratic programming (QP) solver designed to efficiently handle challenging dense and sparse QPs. ODYN combin…
QuantGraph: A Receding-Horizon Quantum Graph Solver
Pranav Vaidhyanathan, Aristotelis Papatheodorou, David R. M. Arvidsson-Shukur +3
Dynamic programming is a cornerstone of graph-based optimization. While effective, it scales unfavorably with problem size. In this work, we present QuantGraph, a two-stage quantum…
Learning Physical Systems: Symplectification via Gauge Fixing in Dirac Structures
Aristotelis Papatheodorou, Pranav Vaidhyanathan, Natalia Ares +1
Physics-informed deep learning has achieved remarkable progress by embedding geometric priors, such as Hamiltonian symmetries and variational principles, into neural networks, enab…
Neural Associative Skill Memories for safer robotics and modelling human sensorimotor repertoires
Pranav Mahajan, Mufeng Tang, T. Ed Li +2
Modern robots face challenges shared by humans, where machines must learn multiple sensorimotor skills and express them adaptively. Equipping robots with a human-like memory of how…