Fatma Yamac

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About

Fatma Yamac is a robotics researcher whose work sits at the intersection of intelligent control systems and parallel mechanisms. Her primary research areas include cable-driven parallel robots (CDPRs), reinforcement learning for motion control, and multi-input multi-output (MIMO) system optimization. Her most impactful contribution to date is the development of a reinforcement learning-based control strategy for planar cable-driven parallel robots, which eliminates the need for traditional tension distribution algorithms—a significant bottleneck in CDPR control. This work, published in 2022 and already garnering 30 citations, demonstrates both point-to-point and dynamic reference position tracking, offering a more adaptive and computationally efficient approach to robot control. By integrating RL into the control loop, Yamac addresses the inherent nonlinearities and redundancies of cable-driven systems, paving the way for more robust and autonomous robotic platforms. Her research is particularly relevant for applications in manufacturing, rehabilitation, and large-scale manipulation, where precise and flexible motion control is critical. With her innovative fusion of machine learning and classical robotics, Fatma Yamac is establishing herself as a rising contributor to the next generation of intelligent robotic systems.