Amin Mozayyan
Papers
2
Total Citations
7
H-Index
1
About
Amin Mozayyan is a robotics researcher focused on advancing the autonomy and dexterity of humanoid robots, with key contributions in simultaneous localization and mapping (SLAM) and motion imitation. His most cited work, "Comparative Evaluation of RGB-D SLAM Methods for Humanoid Robot Localization and Mapping" (2023, 6 citations), systematically benchmarks algorithms like RTAB-Map, ORB-SLAM3, and OpenVSLAM on the SURENA-V humanoid platform, providing critical insights for reliable robot navigation in complex environments. In his 2024 study, "Enhancing Human Motion Imitation in Humanoid Robots," Mozayyan tackles the inverse kinematics challenge for teleoperation, comparing ANN, ANFIS, and GA-optimized ANFIS to enable more natural arm movement replication—a vital step for applications in healthcare and hazardous settings. Though early in his career, his work on the SURENA-V platform demonstrates a strong commitment to bridging perception and control in humanoid robotics. With a growing citation footprint, Mozayyan’s research is laying groundwork for more capable, human-like robots that can operate safely alongside people.
Research Focus
Key Achievements
Top Papers
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