Publications by Maryam Kamgarpour

Learning to Assemble with Alternative Plans

Gabriel Rémi Vallat, Maryam Kamgarpour, Stefana Parascho

We present a reinforcement learning framework for constructing assemblies composed of rigid parts, which are commonly seen in many historical masonry buildings and bridges. Traditional construction methods for such structures often depend on dense scaffold ...

Association for Computing Machinery (ACM)2025

A learning-based approach to stochastic optimal control under reach-avoid constraint

Maryam Kamgarpour

We develop a model-free approach to optimally control stochastic, Markovian systems subject to a reach-avoid constraint. Specifically, the state trajectory must remain within a safe set while reaching a target set within a finite time horizon. Due to the t ...

Association for Computing Machinery2025

On the Convergence of Gradient Descent in Scalar Two-Agent Infinite-Horizon LQ Games

Maryam Kamgarpour

Institute of Electrical and Electronics Engineers (IEEE)2025

Chance-Constrained Linear Quadratic Gaussian Games for Multi-Robot Interaction Under Uncertainty

Maryam Kamgarpour, Kai Ren

Institute of Electrical and Electronics Engineers (IEEE)2025

Safe optimization with grey-box information: Application to composites autoclave processing improvement on the fly

Maryam Kamgarpour

In the manufacture of aerospace-grade composites in the autoclave, the curing process plays a crucial role as it directly governs the quality of the final parts. Maintaining the part's thermal history, namely, thermal lag and exotherm, under predetermined ...

2025

Convergence of a model-free entropy-regularized inverse reinforcement learning algorithm

Maryam Kamgarpour

Given a dataset of expert demonstrations, inverse reinforcement learning (IRL) aims to recover a reward for which the expert is optimal. This work proposes a model-free algorithm to solve the entropy-regularized IRL problem. In particular, we employ a stoc ...

Institute of Electrical and Electronics Engineers Inc.2024

Payoff-Based Learning of Nash Equilibria in Merely Monotone Games

Maryam Kamgarpour

We address learning Nash equilibria in convex games under the payoff information setting. We consider the case in which the game pseudo- gradient is monotone but not necessarily strictly monotone. This relaxation of strict monotonicity enables application ...

Institute of Electrical and Electronics Engineers (IEEE)2024

Aircraft Trajectory Planning for Climate Hotspot Avoidance Considering Air Traffic Complexity: A Constrained Multi-Agent Reinforcement Learning Approach

Maryam Kamgarpour

Planning aircraft trajectories to avoid climate-sensitive areas poses operational challenges, including increased traffic complexity and potential safety risks. This study presents a framework designed to plan operationally feasible climate-friendly routes ...

2024

Multi-Agent Learning in Contextual Games under Unknown Constraints

Anna Maria Maddux, Maryam Kamgarpour

We consider the problem of learning to play a repeated contextual game with unknown reward and unknown constraints functions. Such games arise in applications where each agent’s action needs to belong to a feasible set, but the feasible set is a priori unk ...

2024

Learning to bid in forward electricity markets using a no-regret algorithm

Maryam Kamgarpour

It is a common practice in the current literature of electricity markets to use game-theoretic approaches for strategic price bidding. However, they generally rely on the assumption that the strategic bidders have prior knowledge of rival bids, either perf ...

Elsevier2024

Recursively Feasible Chance-Constrained Model Predictive Control Under Gaussian Mixture Model Uncertainty

Maryam Kamgarpour, Kai Ren

We present a chance-constrained model predictive control (MPC) framework under Gaussian mixture model (GMM) uncertainty. Specifically, we consider the uncertainty that arises from predicting future behaviors of moving obstacles, which may exhibit multiple ...

Institute of Electrical and Electronics Engineers (IEEE)2024

Multi-robot task allocation for safe planning against stochastic hazard dynamics

Orcun Karaca, Maryam Kamgarpour

We address multi-robot safe mission planning in uncertain dynamic environments. This problem arises in several applications including safety-critical exploration, surveillance, and emergency rescue missions. Computation of a multi-robot optimal control pol ...

IEEE2023

Safe Zeroth-Order Optimization Using Linear Programs

Baiwei Guo, Giancarlo Ferrari Trecate, Yuning Jiang, Maryam Kamgarpour

To solve unmodeled optimization problems with hard constraints, this paper proposes a novel zeroth-order approach called Safe Zeroth-order Optimization using Linear Programs (SZO-LP). The SZO-LP method solves a linear program in each iteration to find a de ...

New York2023

Safe Zeroth-Order Convex Optimization Using Quadratic Local Approximations

Baiwei Guo, Giancarlo Ferrari Trecate, Yuning Jiang, Maryam Kamgarpour

We address black-box convex optimization problems, where the objective and constraint functions are not explicitly known but can be sampled within the feasible set. The challenge is thus to generate a sequence of feasible points converging towards an optim ...

IEEE2023

Certification of Bottleneck Task Assignment With Shortest Path Criteria

Maryam Kamgarpour, Tony Alan Wood

Minimising the longest travel distance for a group of mobile robots with interchangeable goals requires knowledge of the shortest length paths between all robots and goal destinations. Determining the exact length of the shortest paths in an environment wi ...

2023

Reinforcement learning for scaffold-free construction of spanning-structures

Gabriel Rémi Vallat, Anna Maria Maddux, Jingwen Wang, Maryam Kamgarpour, Stefana Parascho

In construction robotics, a conventional design-to-fabrication work-flow starts with designing a structure, followed by task and robotic motion planning, and ultimately, fabrication. However, this approach can prove unsuccessful, as we may only discover th ...

New York2023

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

Andreas Krause, Maryam Kamgarpour

We consider model-based multi-agent reinforcement learning, where the environment transition model is unknown and can only be learned via expensive interactions with the environment. We propose H-MARL (Hallucinated Multi-Agent Reinforcement Learning), a no ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Chance-Constrained Trajectory Planning With Multimodal Environmental Uncertainty

Maryam Kamgarpour, Kai Ren

We tackle safe trajectory planning under Gaussian mixture model (GMM) uncertainty. Specifically, we use a GMM to model the multimodal behaviors of obstacles' uncertain states. Then, we develop a mixed-integer conic approximation to the chance-constrained t ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Enabling inter-area reserve exchange through stable benefit allocation mechanisms *

Orcun Karaca, Maryam Kamgarpour, Gabriela Hug

The establishment of a single European day-ahead market has accomplished the integration of the regional day-ahead markets. However, reserve provision and activation remain an exclusive responsibility of regional operators. This limited spatial coordinatio ...

PERGAMON-ELSEVIER SCIENCE LTD2022

System-level, input-output and new parameterizations of stabilizing controllers, and their numerical computation

Luca Furieri, Maryam Kamgarpour

It is known that the set of internally stabilizing controller Cstab is non-convex, but it admits convex characterizations using certain closed-loop maps: a classical result is the Youla parameterization, and two recent notions are the system-level paramete ...

PERGAMON-ELSEVIER SCIENCE LTD2022