Seyed Ali Baradaran Birjandi
Technical University of Munich, Intel (Germany), Robotics Research (United States)
Papers
9
Total Citations
213
H-Index
7
About
Seyed Ali Baradaran Birjandi is a robotics researcher whose work sits at the intersection of robot perception, control, and human-robot interaction. He has made particularly significant contributions to collision detection and monitoring in robot manipulators, most notably through his development of the Observer-Extended Direct Method — the first practical realization of the theoretically optimal direct collision monitoring approach, leveraging proprioception and IMU sensing — which has garnered 64 citations since its publication in 2020. Complementing this, his model-adaptive collision detection framework introduced regressor-based observer methods to compensate for erroneous dynamics models, further advancing detection sensitivity with 49 citations. Birjandi has also advanced state estimation for robotic systems, developing joint velocity and acceleration observers through model-based IMU and encoder fusion, and extending these methods to Cartesian task-space control. His work on passivity-based adaptive force-impedance control addresses cooperative multi-arm manipulation, while his research on haptic motion guidance explores intuitive human-robot collaboration. Contributing to standardization efforts, he co-authored a benchmarking framework for tactile robot performance and safety. Across his career, Birjandi has consistently bridged theoretical rigor with practical implementation, establishing himself as a thoughtful contributor to modern robot manipulation and interaction research.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6Evaluation of Robot Manipulator Link Velocity and Acceleration Observer8 citations · 2023
- 7
- 8Robust Cartesian Kinematics Estimation for Task-Space Control Systems4 citations · 2022
- 9