- Postdoctoral associate, Mechanical engineering, University of Minnesota
- Ph.D. Electrical Engineering Michigan State University
- M.Sc. Mechatronics Engineering, K. N. Toosi University of Technology
- B.Sc. Electrical Engineering, K. N. Toosi University of Technology
- Research interest: control, Reinforcement learning, multi-agent systems, robotics.
Current and Past Work
With tremendous acceleration in electrified mobility (e-mobility), high-level coordination and control and getting the best out of the energy system are crucial. However, multi-domain safety-critical control, and system complexity make it an inherently challenging problem. As a postdoctoral associate, I am currently investigating the design of intelligent control and safe coordination of energy systems inspired by multi-agent techniques.
My research focus during my Ph.D. was to design data-driven, safe, and optimal controllers in the presence of model and environmental uncertainty to enable autonomy in dynamic systems. I developed safe and data-efficient off-policy reinforcement learning algorithms for safety-critical systems using control barrier functions (CBF). To address the safety challenge in exploratory data collection, I proposed employing an efficient learning framework with a prescribed performance in conjunction with a robust formulation to ensure the system’s safety and stability even during data collection and learning. To tackle the safety problem in the face of environmental uncertainty, I proposed a safety-aware model-learning and a novel CBF capable of safety guarantee with simplified modeling of the environment. In addition, to benefit from the cooperation of agents in an uncertain environment, I developed a cooperative framework for collision avoidance in multi-agent systems operating under heterogeneous measurement uncertainty by deploying information-gap theory.
 Marvi, Z, Kiumarsi, B. “Safe reinforcement learning: A control barrier function optimization approach”. Int J Robust Nonlinear Control. 2021; 31: 1923– 1940.
 Marvi, Z, Kiumarsi, “Barrier-certified learning-enabled safe control design for systems operating in uncertain environments,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 1–13, Mar. 2022.
 Marvi, Z, Kiumarsi, B. “Robust Satisficing Cooperative Control Barrier Functions for Multi-Robots Systems using Information-Gap Theory”. Int J Robust Nonlinear Control. 2022.
Marvi and B. Kiumarsi, “Reinforcement Learning with Safety and Stability Guarantees During Exploration for Linear Systems,” in IEEE Open Journal of Control Systems, vol. 1, pp. 322-334, 2022.
 Marvi and B. Kiumarsi, “Safety Planning Using Control Barrier Function: A Model Predictive Control Scheme,” 2019 IEEE 2nd Connected and Automated Vehicles Symposium (CAVS), Honolulu, HI, USA, 2019, pp. 1-5.
 Marvi and B. Kiumarsi, “Safe Off-policy Reinforcement Learning Using Barrier Functions,” 2020 American Control Conference (ACC), Denver, CO, USA, 2020, pp. 2176-2181.
 Marvi and B. Kiumarsi, “Barrier-certified Learning-based Control of Systems with Uncertain Safe Set,” 2021 American Control Conference (ACC), New Orleans, LA, USA, 2021.
 Marvi and B. Kiumarsi, “Barrier-Certified Model-Learning and Control of Uncertain Linear Systems using Experience Replay Method”, 2021 Conference on Decision and Control (CDC), Austin, TX, USA, 2021.
 Marvi, B. Kiumarsi, H. Modares, “High-confidence Barrier-certified Control Design using Goal-oriented Scenario Optimization and Experience Replay Model Learning”, 2022 (Under Review).