Pegah Nomanfar
Papers
2
Total Citations
5
H-Index
2
About
Pegah Nomanfar is an emerging researcher at the intersection of robotics and artificial intelligence, with a primary focus on the control of cable-driven parallel robots (CDPRs) using reinforcement learning. Her work addresses the complex challenge of motion control in these flexible, high-degree-of-freedom systems, where traditional control methods often fall short. Nomanfar’s major contribution lies in pioneering the application of advanced reinforcement learning algorithms—specifically, the Deep Deterministic Policy Gradient (DDPG) multi-agent framework—to achieve precise, adaptive motion control for CDPRs. Her 2024 paper on this topic has already garnered 3 citations, signaling growing interest in her approach. Additionally, her 2023 brief review of reinforcement learning control for CDPRs (2 citations) provides a valuable synthesis of the field, helping to guide future research. By demonstrating that machine learning can effectively interact with and optimize robotic environments, Nomanfar is laying the groundwork for more intelligent, autonomous cable-driven systems. Her work is particularly notable for bridging the gap between theoretical reinforcement learning and practical robotic control, offering a scalable solution for applications in manufacturing, rehabilitation, and large-scale manipulation.
Research Focus
Key Achievements
Top Papers
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- 2