Saba Samadi
Papers
1
Total Citations
10
H-Index
1
About
Saba Samadi is a robotics researcher whose work centers on intelligent control systems, particularly the application of deep reinforcement learning to parallel robot manipulators. Her most-cited paper, "Model-Free Dynamic Control of a 3-DoF Delta Parallel Robot for Pick-and-Place Application based on Deep Reinforcement Learning" (2022, 10 citations), addresses a fundamental challenge in robotics: the difficulty of obtaining accurate dynamic models for complex mechanical systems. Rather than relying on traditional, mathematically intensive identification methods, Samadi pioneered a model-free control approach using deep reinforcement learning. This innovation allows the Delta robot to learn optimal control policies directly from interaction with its environment, significantly simplifying the control pipeline while maintaining high performance in high-speed pick-and-place tasks. Her work bridges the gap between modern machine learning techniques and practical industrial robotics, offering a more accessible and adaptive alternative to classical control methods. By demonstrating that reinforcement learning can effectively handle the nonlinear dynamics of parallel robots, Samadi has contributed to making advanced automation more deployable in real-world manufacturing settings. Her research continues to explore how data-driven methods can replace complex analytical modeling in robotic systems.
Research Focus
Key Achievements
Top Papers
- 1