90 citations · 910 across the 140 of their papers we have counts for
152 papers
Pareto-Optimal Entropy-Regularized Trajectory Optimization
Dimitrios S. Georgiou, Augustinos D. Saravanos, Evangelos A. Theodorou
Trajectory optimization (TO) under nonlinear dynamics, actuation limits and collision avoidance constraints is a fundamental problem in robotics, albeit especially challenging due…
Distributionally Robust Schrödinger Bridge
Jinhwan Sul, Panagiotis Theodoropoulos, Vincent Pacelli +2
Schrödinger bridge (SB) learns stochastic transport between prescribed initial and target distributions. When the initial distribution shifts at test time, the learned dynamics can…
Reinforcement Learning to Accelerate Primal-Dual Hybrid Gradient for Linear Programming
Jinhwan Sul, Alex Oshin, Evangelos A. Theodorou
Primal-dual hybrid gradient (PDHG) methods solve large-scale linear programs (LPs) using GPU-friendly matrix-vector products and projections, but their practical performance depend…
Explore Broadly, Reason Sharply: Push Small Models toward the Frontier via Sampling
Panagiotis Theodoropoulos, Nan Jiang, Xintong Duan +4
Power-sharpened sampling is an inference-time alternative to reinforcement-learning (RL) post-training for enhancing reasoning in large language models (LLMs). High-probability seq…
Variational Inference via Entropic Transport Descent
Vincent Pacelli, Akash Ratheesh, Evangelos Theodorou
Particle-based variational inference (ParVI) methods approximate an intractable target distribution by evolving an ensemble of interacting samples. Existing approaches rely predomi…
Deep-Unfolded Coordination
Hunter Kuperman, Minchan Jung, Rahul V. Ghosh +2
Distributed optimization is a highly scalable and structurally transparent technique to solve multi-agent robotics problems; however, such methods often suffer from the need for hi…
Scalable Deep Unfolding of Conic Optimizers
Alex Oshin, Rahul Vodeb Ghosh, Evangelos A. Theodorou
Deep unfolding (DU) accelerates iterative optimizers by introducing learnable components and training them through unrolled iterations, but extending DU to the large-scale semidefi…
Beyond Pure Sampling: Hybrid Optimization Mechanisms for Non-Convex Model Predictive Control
Yuichiro Aoyama, Minchan Jung, Akash Ratheesh +1
This paper investigates the optimization mechanisms of non-convex Model Predictive Control (MPC) using the Maximum Entropy Differential Dynamic Programming (ME-DDP) framework. Navi…
Multistep Belief Space Dynamics Learning For Risk-Aware Control
Jason Gibson, Bogdan Vlahov, Patrick Spieler +1
As autonomous vehicles move from a simplified research setting to practical use, there exists a large gap between the dynamic behavior of a human driving and an autonomous system.…
Sampling-Based Control via Entropy-Regularized Optimal Transport
Vincent Pacelli, Akash Ratheesh, Evangelos A. Theodorou
Sampling-based model predictive control methods like MPPI and CEM are essential for real-time control of nonlinear robotic systems, particularly where discontinuous dynamics preclu…
Distributed Covariance Steering via Non-Convex ADMM for Large-Scale Multi-Agent Systems
Augustinos D. Saravanos, Isin M. Balci, Arshiya Taj Abdul +2
This paper studies the problem of steering large-scale multi-agent stochastic linear systems between Gaussian distributions under probabilistic collision avoidance constraints. We…
Fundamental Limits for Sensor-Based Control via the Gibbs Variational Principle
Vincent Pacelli, Evangelos A. Theodorou
Fundamental limits on the performance of feedback controllers are essential for benchmarking algorithms, guiding sensor selection, and certifying task feasibility -- yet few genera…
cuNRTO: GPU-Accelerated Nonlinear Robust Trajectory Optimization
Jiawei Wang, Arshiya Taj Abdul, Evangelos A. Theodorou
Robust trajectory optimization enables autonomous systems to operate safely under uncertainty by computing control policies that satisfy the constraints for all bounded disturbance…
Generalized Schrödinger Bridge on Graphs
Panagiotis Theodoropoulos, Juno Nam, Evangelos Theodorou +1
Transportation on graphs is a fundamental challenge across many domains, where decisions must respect topological and operational constraints. Despite the need for actionable polic…
Deep FlexQP: Accelerated Nonlinear Programming via Deep Unfolding
Alex Oshin, Rahul Vodeb Ghosh, Augustinos D. Saravanos +1
We propose FlexQP, an always-feasible convex quadratic programming (QP) solver based on an elastic relaxation of the QP constraints. If the original constraints are feasib…
Asynchronous Distributed Multi-Robot Motion Planning Under Imperfect Communication
Ardalan Tajbakhsh, Augustinos Saravanos, James Zhu +3
This paper addresses the challenge of coordinating multi-robot systems under realistic communication delays using distributed optimization. We focus on consensus ADMM as a scalable…
Distributed Stochastic Search for Multi-Agent Model Predictive Control
Taehyun Yoon, Augustinos D. Saravanos, Evangelos A. Theodorou
Many real-world multi-agent systems exhibit nonlinear dynamics and complex inter-agent interactions. As these systems increase in scale, the main challenges arise from achieving sc…
Optimal Control Theoretic Neural Optimizer: From Backpropagation to Dynamic Programming
Guan-Horng Liu, Tianrong Chen, Evangelos A. Theodorou
Optimization of deep neural networks (DNNs) has been a driving force in the advancement of modern machine learning and artificial intelligence. With DNNs characterized by a prolong…
Scaling Robust Optimization for Swarms: A Distributed Perspective
Arshiya Taj Abdul, Augustinos D. Saravanos, Evangelos A. Theodorou
This article introduces a decentralized robust optimization framework for safe multi-agent control under uncertainty. Although stochastic noise has been the primary form of modelin…
Momentum Multi-Marginal Schrödinger Bridge Matching
Panagiotis Theodoropoulos, Augustinos D. Saravanos, Evangelos A. Theodorou +1
Understanding complex systems by inferring trajectories from sparse sample snapshots is a fundamental challenge in a wide range of domains, e.g., single-cell biology, meteorology,…
Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving
Jacob Levy, Jason Gibson, Bogdan Vlahov +4
High-speed off-road autonomous driving presents unique challenges due to complex, evolving terrain characteristics and the difficulty of accurately modeling terrain-vehicle interac…
Nonlinear Robust Optimization for Planning and Control
Arshiya Taj Abdul, Augustinos D. Saravanos, Evangelos A. Theodorou
This paper presents a novel robust trajectory optimization method for constrained nonlinear dynamical systems subject to unknown bounded disturbances. In particular, we seek optima…
Barrier-Riccati Synthesis for Nonlinear Safe Control with Expanded Region of Attraction
Hassan Almubarak, Maitham F. AL-Sunni, Justin T. Dubbin +3
We present a Riccati-based framework for safety-critical nonlinear control that integrates the barrier states (BaS) methodology with the State-Dependent Riccati Equation (SDRE) app…
Deep Distributed Optimization for Large-Scale Quadratic Programming
Augustinos D. Saravanos, Hunter Kuperman, Alex Oshin +3
Quadratic programming (QP) forms a crucial foundation in optimization, encompassing a broad spectrum of domains and serving as the basis for more advanced algorithms. Consequently,…