Zohreh Raziei
Papers
2
Total Citations
8
H-Index
2
About
Zohreh Raziei is a researcher at the forefront of intelligent manufacturing and automation, whose work is shaping the future of Industry 4.0. Her primary research areas focus on integrating deep reinforcement learning and modular robotics to create adaptable, resilient production systems. Raziei’s major contributions lie in developing frameworks that enable automation to dynamically respond to real-time changes and operational deviations—a critical step beyond rigid, pre-programmed systems. Her most-cited papers, including "Adaptable automation with modular deep reinforcement learning and policy transfer" and "Enabling adaptable Industry 4.0 automation with a modular deep reinforcement learning framework" (both 2021, with 4 citations each), propose novel architectures that combine modular design with policy transfer, allowing robots to learn and adapt without full retraining. This work directly addresses the need for flexible, intelligent sensing and computation in collaborative robotics. Though early in her citation impact, Raziei’s research is foundational for next-generation smart factories, offering practical pathways toward truly autonomous and responsive manufacturing environments. Her contributions are particularly notable for bridging theoretical reinforcement learning with real-world industrial applications, making her a rising voice in the field of adaptive automation.
Research Focus
Key Achievements
Top Papers
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- 2