Samuel Tesfazgi
Papers
4
Total Citations
11
H-Index
2
About
Samuel Tesfazgi is a robotics researcher advancing the safe and adaptive operation of autonomous systems in uncertain, human-centered environments. His work sits at the intersection of motion planning, control, and human-robot interaction, with a strong emphasis on probabilistic and data-driven methods. A key contribution is his development of vision-based, uncertainty-aware motion planning using probabilistic semantic segmentation, enabling robots to navigate cluttered, unpredictable spaces without relying on simplistic Gaussian assumptions—work that has already garnered 5 citations since its 2023 publication. Tesfazgi is also pioneering the integration of Gaussian Process online learning with model-based control, demonstrating how robots can adapt to time-varying dynamics in real time, a critical capability for real-world deployment. In the domain of rehabilitation robotics, he has introduced uncertainty-aware methods for automated arm impedance assessment using exoskeletons, aiming to personalize neurorehabilitation. Most recently, his data-driven force observer for series elastic actuators, leveraging Gaussian Processes, promises safer and more responsive physical human-robot interaction. Through these contributions, Tesfazgi is shaping a future where robots operate not just with precision, but with a principled understanding of their own uncertainty.
Research Focus
Key Achievements
Top Papers
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