Yashar Mousavi

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About

Yashar Mousavi is a robotics researcher advancing the frontier of autonomous systems through optimal control and human-robot collaboration. His primary research areas include optimal control theory, simulation-to-real-world transfer, and safety-critical control for collaborative robots. Mousavi’s most notable contribution is his work on bridging the gap between simulation and reality for wheeled mobile robots, where he developed frameworks that maintain optimal performance and stability despite dynamic uncertainties and unknown disturbances—a fundamental challenge in deploying autonomous systems outside controlled environments. His 2020 paper on this topic has garnered 14 citations, establishing a foundation for robust, real-world robotic control. More recently, Mousavi has focused on safety prioritization in collaborative robotics, introducing iterative feedback linearization control methods that enable robots to operate safely in human-centric environments. His 2024 work, already cited 6 times, addresses the critical need for intelligent systems that can work alongside humans without compromising safety or performance. By tackling both optimality and safety in autonomous systems, Mousavi is contributing to the next generation of robots capable of seamless integration into everyday human spaces, from manufacturing floors to domestic settings.