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<header class="post-header"><h1 class="post-title"><span class="font-weight-bold">Augustinos Saravanos</span>
</h1>
<p class="desc">PhD in Machine Learning Candidate @ <a href="https://sites.gatech.edu/acds/" target="_blank"
rel="noopener noreferrer"> ACDS Lab </a>, Georgia
Tech</p></header>
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<div class="clearfix"><p>I’m a fifth-year <b> PhD in Machine Learning candidate </b> at <b> Georgia
Tech </b>.
I am fortunate to be part of the <a href="https://sites.gatech.edu/acds/" target="_blank"
rel="noopener noreferrer"> Autonomous Control and Decision Systems
Lab </a> and to be advised by <a href="https://scholar.google.com/citations?hl=en&user=dG9MV7oAAAAJ"
target="_blank" rel="noopener noreferrer"> Prof. Evangelos
Theodorou</a>.
<p>My research bridges <b> optimization</b>, <b>machine learning</b> and <b>control theory</b>
towards developing <b> scalable and effective algorithms </b> for <b> large-scale decision-making
systems</b>.
<p>As the scale and complexity of multi-agent systems rapidly increase in various domains such as
robotics, machine learning, transportation networks, resource allocation, power networks, finance,
etc.,
there is an emerging need for building algorithms characterized by <b>scalability</b>, <b>computational/communication
efficiency</b>, <b>robustness under uncertainty</b>, <b>generalizability</b> and <b>interpretability</b>.
</p>
<p> Towards addressing these challenges, my research focuses in constructing <b> <font color="#A0522D">distributed
optimization, control and learning-based architectures </font></b> that enable efficient and
reliable decision-making in large-scale systems. Some representative works are:
</p>
<ul>
<li><b> <font color="#A0522D">Distributed dynamic optimization architectures </font> for large-scale
multi-agent systems</b> [<a href="https://ieeexplore.ieee.org/abstract/document/10288223"
target="_blank" rel="noopener noreferrer"><b>T-RO 2023</b></a>]
</li>
<li><b> <font color="#A0522D">Scalable distribution steering</font> for stochastic multi-agent
systems</b> [<a href="https://www.roboticsproceedings.org/rss17/p075.pdf" target="_blank"
rel="noopener noreferrer"><b>RSS 2021</b></a>, <a
href="https://ieeexplore.ieee.org/document/10802104" target="_blank"
rel="noopener noreferrer"><b>IROS 2024</b></a>]
</li>
<li><b> <font color="#A0522D">Deep learning-based stochastic multi-agent control</font> with
forward-backward SDEs</b> [<a href="https://www.roboticsproceedings.org/rss18/p055.pdf"
target="_blank" rel="noopener noreferrer"><b>RSS 2022</b></a>]
</li>
<li><b> <font color="#A0522D">Hierarchical distribution optimization</font> for very-large-scale
clustered systems </b> [<a href="https://www.roboticsproceedings.org/rss19/p110.pdf"
target="_blank" rel="noopener noreferrer"><b>RSS 2023</b></a>]
</li>
<li><b> <font color="#A0522D">Distributed robust optimization</font> under unknown bounded
uncertainty</b> [<a href="https://arxiv.org/pdf/2402.16227" target="_blank"
rel="noopener noreferrer"><b>Preprint</b></a>]
</li>
<li><b> <font color="#A0522D">Deep learning-aided distributed optimization</font> for large-scale
quadratic programming</b> [<a href="https://arxiv.org/pdf/2412.12156" target="_blank"
rel="noopener noreferrer"><b>ICLR 2025</b></a>]
</li>
</ul>
<p>During my PhD, I also spent a summer at the <a href="https://www.bosch-ai.com/" target="_blank"
rel="noopener noreferrer"> <b> Bosch Center for
Artificial Intelligence </b> </a> as a machine learning research intern, where I worked on <b> model
alignment </b> and <b> federated learning </b>, under the supervision of <a
href="https://scholar.google.com/citations?hl=en&user=kpkMxE8AAAAJ" target="_blank"
rel="noopener noreferrer"> Dr. Wan-Yi Lin</a> and <a
href="https://scholar.google.com/citations?user=6LYI6uUAAAAJ&hl=en" target="_blank"
rel="noopener noreferrer"> Dr. Zhenzhen Li</a>.</p>
<p>Prior to Georgia Tech, I graduated (top 1%) with a Diploma in Electrical and Computer Engineering
from the University of Patras in Greece, advised by <a
href="https://scholar.google.com/citations?hl=en&user=K4FeOr8AAAAJ" target="_blank"
rel="noopener noreferrer"> Prof. Evangelos Papadopoulos</a> and <a
href="http://www.ece.upatras.gr/index.php/en/ece-faculty/koussoulas-nick.html"
target="_blank" rel="noopener noreferrer"> Prof. Nick Koussoulas</a>.</p>
<p>See my <b> <a href="https://asaravanos.github.io/assets/pdf/Saravanos_CV.pdf" target="_blank">full
CV</a> </b> here.</p>
<p><b>
Contact: </b> <a href="mailto:asaravanos3@gatech.edu">asaravanos3 [at] gatech [dot] edu</a></p>
</div>
<p>
<img src="/assets/img/Research_Overview_Figure_v4.png" alt="Transparent PNG Logo"
style="float: right; margin-left: 10px; width: 100%; height: auto;">
</p>
<div class="publications" style="max-width: 100vw; width: 100%;">
<h2 id="selected-publications">Selected Publications</h2>
<p class="intro-text">
* Equal contribution. See <a href="https://scholar.google.com/citations?user=6XP9s1MAAAAJ&hl=en">Google
Scholar</a> for full list of publications.
</p>
<ol class="bibliography">
<li>
<div>
<div class="col-sm-2 abbr"><abbr class="badge" style="width: 120px;"> Preprint </abbr></div>
<div id="saravanos2024deep">
<div class="title"><font size="+1">Deep Distributed Optimization for Large-Scale
Quadratic Programming</font></div>
<div class="author"><strong><font color="#A0522D"> A.D. Saravanos</font></strong>, H.
Kuperman, A. Oshin, A.T. Abdul, V. Pacelli and E.A. Theodorou
</div>
<div class="periodical"> <strong><font color="#3B57D3"> International Conference on Learning Representations (ICLR)</font></strong>, 2025. <font color="#CD853F">[Acceptance rate: 31%]</font></div>
<div class="links"><a class="abstract btn btn-sm z-depth-0" role="button">Abstract</a>
<a href="https://arxiv.org/abs/2412.12156" class="btn btn-sm z-depth-0"
role="button" target="_blank" rel="noopener noreferrer">PDF</a></div>
<div class="abstract hidden"><p>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, as the scale and complexity of modern
applications continue to grow, the development of efficient and reliable QP
algorithms is becoming increasingly vital. In this context, this paper introduces a
novel deep learning-aided distributed optimization architecture designed for
tackling large-scale QP problems. First, we combine the state-of-the-art Operator
Splitting QP (OSQP) method with a consensus approach to derive DistributedQP, a new
method tailored for network-structured problems, with convergence guarantees to
optimality. Subsequently, we unfold this optimizer into a deep learning framework,
leading to DeepDistributedQP, which leverages learned policies to accelerate
reaching to desired accuracy within a restricted amount of iterations. Our approach
is also theoretically grounded through Probably Approximately Correct (PAC)-Bayes
theory, providing generalization bounds on the expected optimality gap for unseen
problems. The proposed framework, as well as its centralized version DeepQP,
significantly outperform their standard optimization counterparts on a variety of
tasks such as randomly generated problems, optimal control, linear regression,
transportation networks and others. Notably, DeepDistributedQP demonstrates strong
generalization by training on small problems and scaling to solve much larger ones
(up to 50K variables and 150K constraints) using the same policy. Moreover, it
achieves orders-of-magnitude improvements in wall-clock time compared to OSQP. The
certifiable performance guarantees of our approach are also demonstrated, ensuring
higher-quality solutions over traditional optimizers.</p></div>
</div>
</div>
</li>
<li>
<div>
<div class="col-sm-2 abbr"><abbr class="badge" style="width: 120px;"> Preprint </abbr></div>
<div id="abdul2024robust">
<div class="title"><font size="+1">Scaling Robust Optimization for Multi-Agent Robotic
Systems: A Distributed Perspective</font></div>
<div class="author"> A.T. Abdul*, <strong><font color="#A0522D"> A.D. Saravanos*</font></strong> and E.A. Theodorou
</div>
<div class="periodical"><font color="#3B57D3"> Preprint (Under review)</font>, 2024.
</div>
<div class="links"><a class="abstract btn btn-sm z-depth-0" role="button">Abstract</a>
<a href="https://arxiv.org/abs/2402.16227" class="btn btn-sm z-depth-0"
role="button" target="_blank" rel="noopener noreferrer">PDF</a> <a
href="https://youtu.be/Q5xghaqt4SQ"
class="btn btn-sm z-depth-0" role="button" target="_blank"
rel="noopener noreferrer">Video</a></div>
<div class="abstract hidden"><p>This paper presents a novel distributed robust
optimization scheme for steering distributions of multi-agent systems under
stochastic and deterministic uncertainty. Robust optimization is a subfield of
optimization which aims in discovering an optimal solution that remains robustly
feasible for all possible realizations of the problem parameters within a given
uncertainty set. Such approaches would naturally constitute an ideal candidate for
multi-robot control, where in addition to stochastic noise, there might be exogenous
deterministic disturbances. Nevertheless, as these methods are usually associated
with significantly high computational demands, their application to multi-agent
robotics has remained limited. The scope of this work is to propose a scalable
robust optimization framework that effectively addresses both types of
uncertainties, while retaining computational efficiency and scalability. In this
direction, we provide tractable approximations for robust constraints that relevant
in multi-robot settings. Subsequently, we demonstrate how computations can be
distributed through an Alternating Direction Method of Multipliers (ADMM) approach
towards achieving scalability and communication efficiency. Simulation results
highlight the performance of the proposed algorithm in effectively handling both
stochastic and deterministic uncertainty in multi-robot systems. The scalability of
the method is also emphasized by showcasing tasks with up to 100 agents. The results
of this work indicate the promise of blending robust optimization, distribution
steering and distributed optimization towards achieving scalable, safe and robust
multi-robot control.</p></div>
</div>
</div>
</li>
<li>
<div>
<div class="col-sm-2 abbr"><abbr class="badge" style="width: 120px;">IROS 2024 </abbr></div>
<div id="saravanos2022distributed_mpcs">
<div class="title"><font size="+1">Distributed Model Predictive Covariance
Steering</font></div>
<div class="author"><strong><font color="#A0522D"> A.D. Saravanos</font></strong>, I.M.
Balci, E. Bakolas, and E.A. Theodorou
</div>
<div class="periodical"> <strong><font color="#3B57D3">IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS)</font></strong>, 2024.
</div>
<div class="links"><a class="abstract btn btn-sm z-depth-0" role="button">Abstract</a>
<a href="https://arxiv.org/abs/2212.00398" class="btn btn-sm z-depth-0"
role="button" target="_blank" rel="noopener noreferrer">PDF</a> <a
href="https://youtu.be/tzWqOzuj2kQ"
class="btn btn-sm z-depth-0" role="button" target="_blank"
rel="noopener noreferrer">Video</a></div>
<div class="abstract hidden"><p>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 steering theory, distributed optimization and model predictive control (MPC) into a single framework that is safe,
scalable and decentralized. Initially, we pose a problem formulation that uses the Wasserstein distance to steer the state distributions of a multi-agent system to
desired targets, and probabilistic constraints to ensure safety. We then transform this problem into a finite-dimensional optimization one by utilizing a disturbance
feedback policy parametrization for covariance steering and a tractable approximation of the safety constraints. To solve the latter problem, we derive a decentralized
consensus-based algorithm using the Alternating Direction Method of Multipliers. This method is then extended to a receding horizon form, which yields the proposed
DiMPCS algorithm. Simulation experiments on a variety of multi-robot tasks with up to hundreds of robots demonstrate the effectiveness of DiMPCS. The superior scalability
and performance of the proposed method is also highlighted through a comparison against related stochastic MPC approaches. Finally, hardware results on a multi-robot
platform also verify the applicability of DiMPCS on real systems.</p></div>
</div>
</div>
</li>
<li>
<div>
<div class="col-sm-2 abbr"><abbr class="badge" style="width: 120px;">IEEE Transactions <br>
on Robotics </abbr></div>
<div id="saravanos2022distributed_ddp">
<div class="title"><font size="+1">Distributed Differential Dynamic Programming
Architectures for Large-Scale Multi-Agent Control</font></div>
<div class="author"><strong><font color="#A0522D"> A.D. Saravanos</font></strong>, Y.
Aoyama, H. Zhu, and E. A. Theodorou
</div>
<div class="periodical"> <strong><font color="#3B57D3">IEEE Transactions on Robotics (T-RO)</font></strong>, 2023. <font color="#CD853F">[Acceptance rate: ~18%]</font></div>
<div class="links"><a class="abstract btn btn-sm z-depth-0" role="button">Abstract</a>
<a href="https://arxiv.org/abs/2207.13255" class="btn btn-sm z-depth-0"
role="button" target="_blank" rel="noopener noreferrer">PDF</a> <a
href="https://www.youtube.com/watch?v=tluvENcWldw"
class="btn btn-sm z-depth-0" role="button" target="_blank"
rel="noopener noreferrer">Video</a></div>
<div class="abstract hidden"><p>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 architectures that inherit the
computational efficiency and scalability of Differential Dynamic Programming (DDP)
and the distributed nature of the Alternating Direction Method of Multipliers
(ADMM). In this direction, two frameworks are introduced. The first one called
Nested Distributed DDP (ND-DDP), is a three-level architecture which employs ADMM
for enforcing a consensus between all agents, an Augmented Lagrangian layer for
satisfying local constraints and DDP as each agent’s optimizer. In the second
approach, both consensus and local constraints are handled with ADMM, yielding a
two-level architecture called Merged Distributed DDP (MD-DDP), which further reduces
computational complexity. Both frameworks are fully decentralized since all
computations are parallelizable among the agents and only local communication is
necessary. Simulation results that scale up to thousands of vehicles and hundreds of
drones verify the effectiveness of the methods. Superior scalability to large-scale
systems against centralized DDP and centralized/decentralized sequential quadratic
programming is also illustrated. Finally, hardware experiments on a multi-robot
platform demonstrate the applicability of the proposed algorithms, while
highlighting the importance of optimizing for feedback policies to increase
robustness against uncertainty. <a href="https://youtu.be/tluvENcWldw"
target="_blank" rel="noopener noreferrer">A video
with all results is available here</a>.</p></div>
</div>
</div>
</li>
<li>
<div>
<div class="col-sm-2 abbr"><abbr class="badge" style="width: 120px;">RSS 2023 </abbr></div>
<div id="saravanos2023hierarchical">
<div class="title"><font size="+1">Distributed Hierarchical Distribution Control for
Very-Large-Scale Clustered Multi-Agent Systems</font></div>
<div class="author"><strong><font color="#A0522D"> A.D. Saravanos</font></strong>, Y.
Li and E.A. Theodorou
</div>
<div class="periodical"> <strong><font color="#3B57D3">Robotics: Science and Systems (RSS)</font></strong>, 2023. <font color="#CD853F">[Acceptance rate: 33%]</font> </div>
<div class="links"><a class="abstract btn btn-sm z-depth-0" role="button">Abstract</a>
<a href="https://www.roboticsproceedings.org/rss19/p110.html"
class="btn btn-sm z-depth-0" role="button" target="_blank"
rel="noopener noreferrer">PDF</a> <a
href="https://www.youtube.com/watch?v=0QPyR4bD2q0"
class="btn btn-sm z-depth-0" role="button" target="_blank"
rel="noopener noreferrer">Video</a></div>
<div class="abstract hidden"><p>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 Systems (VLMAS) with dimensionality
that can scale up to the order of millions of agents. In particular, we consider the
problem of steering the state distributions of all agents of a VLMAS to prescribed
target distributions while satisfying probabilistic safety guarantees. Based on the
key assumption that such systems often admit a multi-level hierarchical clustered
structure - where the agents are organized into cliques of different levels - we
associate the control of such cliques with the control of distributions, and
introduce the Distributed Hierarchical Distribution Control (DHDC) framework. The
proposed approach consists of two sub-frameworks. The first one, Distributed
Hierarchical Distribution Estimation (DHDE), is a bottom-up hierarchical
decentralized algorithm which links the initial and target configurations of the
cliques of all levels with suitable Gaussian distributions. The second part,
Distributed Hierarchical Distribution Steering (DHDS), is a top-down hierarchical
distributed method that steers the distributions of all cliques and agents from the
initial to the targets ones assigned by DHDE. Simulation results that scale up to
two million agents demonstrate the effectiveness and scalability of the proposed
framework. The increased computational efficiency and safety performance of DHDC
against related methods is also illustrated. The results of this work indicate the
importance of hierarchical distribution control approaches towards achieving safe
and scalable solutions for the control of VLMAS.</p></div>
</div>
</div>
</li>
<li>
<div>
<div class="col-sm-2 abbr"><abbr class="badge" style="width: 120px;">RSS 2022 </abbr></div>
<div id="pereira2022decentralized">
<div class="title"><font size="+1">Decentralized Safe Multi-agent Stochastic Optimal
Control using Deep FBSDEs and ADMM</font></div>
<div class="author"> M.A. Pereira*, <strong><font color="#A0522D"> A.D. Saravanos*</font></strong>, O. So, and E.A. Theodorou
</div>
<div class="periodical"> <strong><font color="#3B57D3">Robotics: Science and Systems (RSS)</font></strong>, 2022. <font color="#CD853F">[Acceptance rate: 31%]</font> </div>
<div class="links"><a class="abstract btn btn-sm z-depth-0" role="button">Abstract</a>
<a href="http://www.roboticsproceedings.org/rss18/p055.html"
class="btn btn-sm z-depth-0" role="button" target="_blank"
rel="noopener noreferrer">PDF</a> <a
href="https://www.youtube.com/watch?v=qjPLUlaxJos"
class="btn btn-sm z-depth-0" role="button" target="_blank"
rel="noopener noreferrer">Video</a></div>
<div class="abstract hidden"><p>In this work, we propose a novel safe and scalable
decentralized solution for multi-agent control in the presence of stochastic. Safety
is mathematically encoded using stochastic control barrier functions and safe
controls are computed by solving quadratic programs. Decentralization is achieved by
augmenting to each agent’s optimization variables, copy variables, for its
neighboring agents. This allows us to decouple the centralized multi-agent
optimization problem. However, to ensure safety, neighboring agents must agree on
what is safe for both of us and this creates a need for consensus. To enable safe
consensus solutions, we incorporate an ADMM-based approach. Specifically, we propose
a Merged CADMM-OSQP implicit neural network layer, that solves a mini-batch of both,
local quadratic programs as well as the overall consensus problem, as a single
optimization problem. This layer is embedded within a Deep FBSDEs network
architecture at every time step, to facilitate end-to-end differentiable, safe and
decentralized stochastic optimal control. The efficacy of the proposed approach is
demonstrated on several challenging multi-robot tasks in simulation. By imposing
requirements on safety specified by collision avoidance constraints, the safe
operation of all agents is ensured during the entire training process. We also
demonstrate superior scalability in terms of computational and memory savings as
compared to a centralized approach.</p></div>
</div>
</div>
</li>
<li>
<div>
<div class="col-sm-2 abbr"><abbr class="badge" style="width: 120px;">RSS 2021 </abbr></div>
<div id="saravanos2021distributed">
<div class="title"><font size="+1">Distributed Covariance Steering with Consensus ADMM
for Stochastic Multi-Agent Systems</font></div>
<div class="author"><strong><font color="#A0522D"> A.D. Saravanos</font></strong>, A.
Tsolovikos, E. Bakolas, and E.A. Theodorou
</div>
<div class="periodical"> <strong><font color="#3B57D3"> Robotics: Science and Systems (RSS)</font></strong>, 2021. <font color="#CD853F">[Acceptance rate: 32%]</font></div>
<div class="links"><a class="abstract btn btn-sm z-depth-0" role="button">Abstract</a>
<a href="http://www.roboticsproceedings.org/rss17/p075.html"
class="btn btn-sm z-depth-0" role="button" target="_blank"
rel="noopener noreferrer">PDF</a> <a
href="https://www.youtube.com/watch?v=RiLQ2P1WXHQ"
class="btn btn-sm z-depth-0" role="button" target="_blank"
rel="noopener noreferrer">Video</a></div>
<div class="abstract hidden"><p>In this paper, we address the problem of steering a team
of agents under stochastic linear dynamics to prescribed final state means and
covariances. The agents operate in a common environment where inter-agent
constraints may also be present. In order for our method to be scalable to
large-scale systems and computationally efficient, we approach the problem in a
distributed control framework using the Alternating Direction Method of Multipliers
(ADMM). Each agent solves its own covariance steering problem in parallel, while
additional copy variables for its closest neighbors are introduced to ensure that
the inter-agent constraints will be satisfied. The inclusion of these additional
variables creates a requirement for consensus between original and copy variables
that involve the same agent. For this reason, we employ a variation of ADMM for
consensus optimization. Simulation results on multi-vehicle systems under
uncertainty with collision avoidance constraints illustrate the effectiveness of our
algorithm. The substantially improved scalability of our distributed approach with
respect to the number of agents is also demonstrated, in comparison with an
equivalent centralized scheme.</p></div>
</div>
</div>
</li>
</ol>
</div>
</article>
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