Papers
5
Total Citations
28
H-Index
3
About
Seyedhassan Zabihifar is a robotics researcher whose work sits at the intersection of intelligent control, motion planning, and autonomous manipulation. His primary research areas include hybrid force/position control, deep reinforcement learning, and computer vision for robotic systems. Zabihifar’s most cited work, “Hybrid Force/Position Control of a Collaborative Parallel Robot Using Adaptive Neural Network” (13 citations), demonstrates his expertise in enabling robots to safely interact with their environment through adaptive control strategies. He has also made notable contributions to practical automation, as evidenced by his work on “Coinbot,” which applies deep reinforcement learning to automate the physically demanding task of moving heavy currency bags in bank cash centers—a real-world application of AI to solve labor-intensive industrial challenges. His research on constrained motion planning for vision-based laser cutting manipulators and continuous learning for object detection further showcases his commitment to developing robots that can operate safely and adaptively in unstructured, human-centric environments. With recent work on memory-efficient path planning for humanoid robots, Zabihifar continues to push the boundaries of autonomous manipulation, making his research highly relevant for students and engineers interested in intelligent, safe, and practical robotic systems.
Research Focus
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