From the 1 of 6 linked papers with an AI index.
6 papers
CaLiSym: Learning Symplectic Dynamics of Real-World Systems through Structured Canonical Lifts
Aristotelis Papatheodorou, Pranav Vaidhyanathan, Natalia Ares +2
The paper introduces CaLiSym, a framework that learns symplectic dynamics for real-world robotic systems by embedding states and ports into a lifted phase space, enabling accurate…
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…
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…
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…
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…