6 citations · 7 across the 17 of their papers we have counts for
20 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…
Bayesian Active Learning for Intent Disambiguation in Interactive Robot Planning
Huao Li, Carson Sobolewski, Augustinos Saravanos +3
Interactive robot planning requires robots to infer and execute human intentions from natural language instructions that are often ambiguous, incomplete, or underspecified. Althoug…
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…
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…
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,…
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…
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,…
Operator Splitting Covariance Steering for Safe Stochastic Nonlinear Control
Akash Ratheesh, Vincent Pacelli, Augustinos D. Saravanos +1
This paper presents a novel algorithm for solving distribution steering problems featuring nonlinear dynamics and chance constraints. Covariance steering (CS) is an emerging method…
Second-Order Constrained Dynamic Optimization
Yuichiro Aoyama, Oswin So, Augustinos D. Saravanos +1
This paper provides an overview, analysis, and comparison of second-order dynamic optimization algorithms, i.e., constrained Differential Dynamic Programming (DDP) and Sequential Q…
Scaling Robust Optimization for Multi-Agent Robotic Systems: A Distributed Perspective
Arshiya Taj Abdul, Augustinos D. Saravanos, Evangelos A. Theodorou
This paper presents a novel distributed robust optimization scheme for steering distributions of multi-agent systems under stochastic and deterministic uncertainty. Robust optimiza…
Distributed Hierarchical Distribution Control for Very-Large-Scale Clustered Multi-Agent Systems
Augustinos D. Saravanos, Yihui Li, Evangelos A. Theodorou
As the scale and complexity of multi-agent robotic systems are subject to a continuous increase, this paper considers a class of systems labeled as Very-Large-Scale Multi-Agent Sys…
Improved Exploration for Safety-Embedded Differential Dynamic Programming Using Tolerant Barrier States
Joshua E. Kuperman, Hassan Almubarak, Augustinos D. Saravanos +1
In this paper, we introduce Tolerant Discrete Barrier States (T-DBaS), a novel safety-embedding technique for trajectory optimization with enhanced exploratory capabilities. The pr…
Distributed Model Predictive Covariance Steering
Augustinos D. Saravanos, Isin M. Balci, Efstathios Bakolas +1
This paper proposes Distributed Model Predictive Covariance Steering (DiMPCS) for multi-agent control under stochastic uncertainty. The scope of our approach is to blend covariance…
Sampling-Based Optimization for Multi-Agent Model Predictive Control
Ziyi Wang, Augustinos D. Saravanos, Hassan Almubarak +2
We systematically review the Variational Optimization, Variational Inference and Stochastic Search perspectives on sampling-based dynamic optimization and discuss their connections…
Distributed Differential Dynamic Programming Architectures for Large-Scale Multi-Agent Control
Augustinos D. Saravanos, Yuichiro Aoyama, Hongchang Zhu +1
In this paper, we propose two novel decentralized optimization frameworks for multi-agent nonlinear optimal control problems in robotics. The aim of this work is to suggest archite…
Decentralized Safe Multi-agent Stochastic Optimal Control using Deep FBSDEs and ADMM
Marcus A. Pereira, Augustinos D. Saravanos, Oswin So +1
In this work, we propose a novel safe and scalable decentralized solution for multi-agent control in the presence of stochastic disturbances. Safety is mathematically encoded using…
Receding Horizon Differential Dynamic Programming Under Parametric Uncertainty
Yuichiro Aoyama, Augustinos D. Saravanos, Evangelos A. Theodorou
Generalized Polynomial Chaos (gPC) theory has been widely used for representing parametric uncertainty in a system, thanks to its ability to propagate uncertainty evolution. In an…