6 papers · 1 filter
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…
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…
Gaitor: Learning a Unified Representation Across Gaits for Real-World Quadruped Locomotion
Alexander L. Mitchell, Wolfgang Merkt, Aristotelis Papatheodorou +2
The current state-of-the-art in quadruped locomotion is able to produce a variety of complex motions. These methods either rely on switching between a discrete set of skills or lea…
Momentum-Aware Trajectory Optimisation using Full-Centroidal Dynamics and Implicit Inverse Kinematics
Aristotelis Papatheodorou, Wolfgang Merkt, Alexander L. Mitchell +1
The current state-of-the-art gradient-based optimisation frameworks are able to produce impressive dynamic manoeuvres such as linear and rotational jumps. However, these methods, w…