Iman Jebellat
Papers
3
Total Citations
34
H-Index
2
About
Iman Jebellat is a rising researcher at the intersection of robotics, reinforcement learning, and micro-scale locomotion. Their work spans two seemingly disparate domains—microrobotics and heavy machinery—united by a common thread of intelligent motion control. Jebellat’s most impactful contribution, "A Reinforcement Learning Approach to Find Optimal Propulsion Strategy for Microrobots Swimming at Low Reynolds Number" (2024, 22 citations), pioneers the use of RL to navigate the viscous, inertia-free world where conventional swimming fails. This work offers a data-driven path to designing more efficient medical and environmental microrobots. In parallel, Jebellat addresses a practical industrial challenge in "Trajectory Generation with Dynamic Programming for End-Effector Sway Damping of Forestry Machine" (2023, 10 citations), developing algorithms to suppress dangerous oscillations in log-loading cranes—a problem that costs time, damages equipment, and risks operator safety. By applying dynamic programming to passive-joint systems, this work directly improves the efficiency and safety of forestry operations. With a growing citation record and a portfolio that bridges fundamental physics and applied robotics, Jebellat demonstrates a rare ability to translate complex control theory into real-world solutions, positioning them as a versatile and promising voice in modern robotics research.
Research Focus
Key Achievements
Top Papers
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