Peyman Setoodeh
Papers
6
Total Citations
138
H-Index
5
About
Peyman Setoodeh is a leading researcher in cooperative telerobotics and adaptive control systems, with a career spanning foundational work in multi-robot teleoperation to cutting-edge advances in interpretable artificial intelligence. His seminal 2005 paper on multi-operator/multi-robot teleoperation (61 citations) introduced an adaptive nonlinear control architecture enabling multiple master-slave pairs to share a collaborative environment—a breakthrough that laid the groundwork for modern cooperative robotic systems. Expanding this framework, he developed discrete-time multi-model control strategies to address time-delay challenges in cooperative teleoperation (17 citations), directly impacting real-world applications in remote surgery and hazardous environment manipulation. More recently, Setoodeh has pioneered the intersection of cognitive development theory and machine learning, as evidenced by his 2021 work on interpretable reinforcement learning inspired by Piaget’s theory (5 citations). This novel approach challenges opaque deep RL algorithms by grounding learning in developmental psychology, offering transparency and robustness for safety-critical systems. His contributions to T-S fuzzy tracking control for constrained time-delay systems (15 citations) further demonstrate his versatility in tackling complex nonlinear dynamics. With over 138 total citations across his most influential works, Setoodeh continues to shape the future of intelligent, human-robot collaboration.
Research Focus
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Top Papers
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