Amirhosein Vedadi
Papers
3
Total Citations
15
H-Index
2
About
Amirhosein Vedadi is a robotics researcher whose work centers on humanoid locomotion, state estimation, and perception. His primary contributions lie in advancing bipedal walking through trajectory optimization, specifically by exploiting the full dynamics of the Divergent Component of Motion (DCM) and Linear Inverted Pendulum Model (LIPM) to generate efficient, online gait patterns. Vedadi has also made significant strides in localization and mapping, conducting a comparative evaluation of RGB-D SLAM algorithms—including RTAB-Map, ORB-SLAM3, and OpenVSLAM—on the SURENA-V humanoid robot, demonstrating how these methods perform under real-world circular path following. Additionally, he has developed a Right Invariant Extended Kalman Filter (RIEKF) for kinematic base state estimation, leveraging Lie group theory to robustly estimate the robot’s position, velocity, and orientation. With his most-cited papers accumulating over 15 citations, Vedadi’s work is foundational for researchers seeking to improve humanoid autonomy and stability. His achievements include hands-on implementation on the Surena V platform, bridging theoretical control and practical robotics.
Research Focus
Key Achievements
Top Papers
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