About

Naira Hovakimyan is the W. Grafton and Lillian B. Wilkins Professor of Mechanical Science and Engineering at the University of Illinois Urbana-Champaign (UIUC), where she also serves as the Director of the AVIATE Center. She holds a Ph.D. in Physics and Mathematics from the Russian Academy of Sciences and an M.S. in Applied Mathematics from Yerevan State University. Her research focuses on machine learning, human-robot interaction for elderly care, safety-critical systems across aerospace, electrical, mechanical, biomedical, and petroleum engineering, multi-vehicle unmanned systems, cyber-physical systems, energy systems, human-robotic space exploration, control of anesthesia, and control in oil production. She has co-authored two books, holds thirteen patents, and has published over 500 refereed articles. Her distinguished contributions have earned her numerous awards, including the AIAA Mechanics and Control of Flight Award, SWE Achievement Award, IEEE CSS Award for Technical Excellence in Aerospace Controls, and the AIAA Pendray Aerospace Literature Award. She is a Fellow of AIAA, IEEE, ASME, and a senior member of NAI. Additionally, she is the cofounder and chief scientist of Intelinair. Her work has been featured in prominent media outlets such as The New York Times, Fox TV, and CNBC.

Research topics

• Computer Science
• Artificial Intelligence
• Computer vision
• Machine Learning
• Data Mining
• Computer Security
• Geography
• Mathematics
• Engineering
• Distributed computing
• Computer network
• Agronomy
• Mathematical optimization
• Ecology
• Control engineering
• Algorithm

Selected publications

• Moving Horizon Estimation for Quadrotors: An L ₁ Adaptive Optimizer Approach

  2026-01-08

  articleSenior author

  Moving Horizon Estimation (MHE) is a state estimation method based on finite-horizon optimization that can offer higher accuracy at the cost of increased computation compared to Kalman filter-based approaches. We present a linear smoothing MHE formulation as a dense Quadratic Program (QP), and a solver consisting of a continuous-time Newton’s method augmented with the L1 Adaptive Optimizer (L1-AO). While MHE is inherently time-varying, conventional approaches treat it as a sequence of independent, time-invariant problems and employ iterative solvers at each time step, which can be both inaccurate and computationally burdensome. In contrast, time-varying solvers track the optimal solution with fewer iterations by exploiting the temporal evolution of the problem, thereby reducing the computational load. In this research, we enhance both the performance and efficiency of MHE through a time-varying solver with an L1-AO augmentation that compensates for the prediction inaccuracy, which is common in practice due to noisy sensors and the lack of prior knowledge of the system. Simulation results on a quadrotor platform show that the L1-AO-augmented approach solves the MHE optimization problem more efficiently than the baseline time-invariant solver and achieves higher estimation accuracy under challenging conditions, compared with both the Extended Kalman Filter and the standard MHE.

  PublisherDOI

• A Mean-Field Game Model For Large-Scale Attrition in Attacker-Defender Systems

  2026-01-01

  articleOpen access
  PublisherDOI

• A Mean-Field Game Model For Large-Scale Attrition in Attacker-Defender Systems

  ArXiv.org · 2026-04-02

  articleOpen access

  This paper proposes a novel Mean-Field Game (MFG) framework for large-scale attacker-defender systems aimed at protecting one or multiple High-Value Units (HVUs). Motivated by classical agent-wise attrition models, we introduce a population-wise attrition mechanism formulated by statistical distance between populations, enabling a macroscopic description of weapon-based interactions between large populations. Leveraging this and Lions derivative on the space of probability measures, we derive the associated MFG system, which characterizes optimal strategies and the evolution of population distributions in attacker-defender interactions. We analyze the model by establishing upper and lower bounds on the defender density, ensuring physical consistency by preventing concentration and depletion. For numerical investigation, we develop a numerical scheme combining physics-informed neural networks with Sinkhorn method to solve attacker-defender MFG system. Simulations confirm the effectiveness of the framework and reveal key insights, including sensitivity to weapon strengths and population dispersion.

  PublisherOA PDF

• Distributed Optimal Defensive Trajectory Planning against Adversarial Swarms with Receding Horizon

  2026-01-08

  articleSenior author

  This paper presents a distributed optimal trajectory planning algorithm for defending drones against adversarial entities that attack a high-value unit (HVU). A single defending drone by itself cannot achieve the collective objective, which is to maintain the survival probability of the HVU close to one. This is because the objective depends on the other defending drones' strategies as well. The proposed Nash equilibrium seeking distributed optimization enables each defending drone to calculate its own optimal trajectory exchanging information with its neighbors, which leads to achieving the collective objective. Further, since the attacking drones' dynamics are not exactly known in general, each defending drone calculates and updates its optimal trajectory at every sampling time with a receding time horizon.

  PublisherDOI

• PACT: Potential Differential Game-Based Multi-Agent Coordination for Time-Critical Missions

  2026-01-08

  articleSenior author

  Increased interest in utilizing low-altitude airspace has sparked the deployment of multi-agent unmanned aerial vehicles (UAVs) to perform a wide range of tasks. Time-critical coordination (TCC) tasks, such as surveillance, environmental monitoring, perimeter defense, entertainment, light shows, and flying ad-hoc networks, represent key emerging uses of low-altitude airspace. Successful execution of such complex missions hinges on tightly synchronized execution, making efficient multi-agent UAV cooperation essential. In this work, we formulate the TCC problem for a fixed set of N UAVs as a potential differential game, enabling all agents' objectives to be unified through a single potential function. By decoupling spatial motion from temporal evolution, the TCC formulation reduces the coordination task to a one-dimensional time-synchronization problem. Leveraging this structure, we construct a potential function that captures the coordination goal, characterize conditions under which equilibria exist for the resulting N-agent potential game, and show that computing the equilibrium reduces to solving a single optimal control problem. Finally, we develop an iterative receding-horizon algorithm to compute the solution of potential game efficiently and demonstrate its effectiveness through simulation results.

  PublisherDOI

• An information-theoretic analysis of continuous-time control and filtering limitations by the I-MMSE relationships

  Automatica · 2026-05-19 · 2 citations

  preprintOpen accessSenior author
  PublisherOA PDFDOI

• A Mean-Field Game Model For Large-Scale Attrition in Attacker-Defender Systems

  arXiv (Cornell University) · 2026-04-02

  preprintOpen access

  This paper proposes a novel Mean-Field Game (MFG) framework for large-scale attacker-defender systems aimed at protecting one or multiple High-Value Units (HVUs). Motivated by classical agent-wise attrition models, we introduce a population-wise attrition mechanism formulated by statistical distance between populations, enabling a macroscopic description of weapon-based interactions between large populations. Leveraging this and Lions derivative on the space of probability measures, we derive the associated MFG system, which characterizes optimal strategies and the evolution of population distributions in attacker-defender interactions. We analyze the model by establishing upper and lower bounds on the defender density, ensuring physical consistency by preventing concentration and depletion. For numerical investigation, we develop a numerical scheme combining physics-informed neural networks with Sinkhorn method to solve attacker-defender MFG system. Simulations confirm the effectiveness of the framework and reveal key insights, including sensitivity to weapon strengths and population dispersion.

  PublisherDOI

• Wasserstein Distributionally Robust Adaptive Covariance Steering

  ArXiv.org · 2025-09-04

  preprintOpen access

  We present a methodology for predictable and safe covariance steering control of uncertain nonlinear stochastic processes. The systems under consideration are subject to general uncertainties, which include unbounded random disturbances (aleatoric uncertainties) and incomplete model knowledge (state-dependent epistemic uncertainties). These general uncertainties lead to temporally evolving state distributions that are entirely unknown, can have arbitrary shapes, and may diverge unquantifiably from expected behaviors, leading to unpredictable and unsafe behaviors. Our method relies on an $\mathcal{L}_1$-adaptive control architecture that ensures robust control of uncertain stochastic processes while providing Wasserstein metric certificates in the space of probability measures. We show how these distributional certificates can be incorporated into the high-level covariance control steering to guarantee safe control. Unlike existing distributionally robust planning and control methodologies, our approach avoids difficult-to-verify requirements like the availability of finite samples from the true underlying distribution or an a priori knowledge of time-varying ambiguity sets to which the state distributions are assumed to belong.

  PublisherOA PDFDOI

• Coordinated Path Following of UAVs using Event-Triggered Communication over Networks with Digraph Topologies

  2025-07-08 · 1 citations

  articleSenior author

  This article presents a novel time-coordination algorithm based on event-triggered communication to ensure multiple UAVs progress along their desired paths in coordination with one another. In the proposed algorithm, a UAV transmits its progression information to its neighbor UAVs only when a decentralized trigger condition is satisfied. Consequently, it significantly reduces the volume of inter-vehicle communications required to achieve the goal compared with the existing algorithms based on continuous communication. With such intermittent communications, it is shown that a decentralized coordination controller guarantees exponential convergence of the coordination error to a neighborhood of zero. Furthermore, a lower bound on the difference between two consecutive event-triggered times is provided showing that the Zeno behavior is excluded with the proposed algorithm. Lastly, simulation results validate the efficacy of the proposed algorithm.

  PublisherDOI

• DiffCoTune: Differentiable Co-Tuning for Cross-Domain Robot Control

  IEEE Robotics and Automation Letters · 2025-09-16

  article

  The deployment of robot controllers is hindered by modeling discrepancies due to necessary simplifications for computational tractability or inaccuracies in data-generating simulators. Such discrepancies typically require ad-hoc tuning to meet the desired performance, thereby ensuring successful transfer to a target domain. We propose a framework for automated, gradient-based tuning to enhance performance in the deployment domain by leveraging differentiable simulators. Our method collects rollouts in an iterative manner to co-tune the simulator and controller parameters, enabling systematic transfer within a few trials in the deployment domain. Specifically, we formulate multi-step objectives for tuning and employ alternating optimization to effectively adapt the controller to the deployment domain. The scalability of our framework is demonstrated by co-tuning model-based and learning-based controllers of arbitrary complexity, ranging from low-dimensional cart-pole stabilization to high-dimensional quadruped and biped tracking, showing performance improvements across different deployment domains.

  PublisherDOI

Recent grants

• EAGER: Human centered robotic system design

  NSF · $300k · 2015–2018

• Distributionally Robust Adaptive Control: Enabling Safe and Robust Reinforcement Learning

  NSF · $375k · 2022–2026

• CPS: Medium: Collaborative Research: Against Coordinated Cyber and Physical Attacks: Unified Theory and Technologies

  NSF · $700k · 2017–2021

• NRI: INT: COLLAB: Synergetic Drone Delivery Network in Metropolis

  NSF · $1.0M · 2018–2022

• NSF-AoF: RI: Small: Safe Reinforcement Learning in Non-Stationary Environments With Fast Adaptation and Disturbance Prediction

  NSF · $500k · 2021–2025

Frequent coauthors

• Chengyu Cao

  University of Connecticut

  119 shared

• Enric Xargay

  University of Michigan–Ann Arbor

  71 shared

• Isaac Kaminer

  Naval Postgraduate School

  54 shared

• Venanzio Cichella

  University of Iowa

  50 shared

• Petros G. Voulgaris

  49 shared

• Eugene Lavretsky

  46 shared

• Anthony Calise

  40 shared

• Aditya Gahlawat

  University of Illinois Urbana-Champaign

  39 shared

Awards & honors

• AIAA Mechanics and Control of Flight Award (2011)
• SWE Achievement Award (2015)
• IEEE CSS Award for Technical Excellence in Aerospace Control…
• AIAA Pendray Aerospace Literature Award (2019)
• Humboldt Prize for lifetime achievements (2014)