Saeed Saeedvand
University of Tabriz, National Taiwan Normal University, Khazar University
Papers
16
Total Citations
358
H-Index
9
About
Saeed Saeedvand is a leading researcher in humanoid robotics, artificial intelligence, and multi-robot systems, with a particular focus on integrating deep reinforcement learning (DRL) into complex robotic control and navigation. His most influential work, a comprehensive survey on humanoid robot development (106 citations), has become a foundational reference for the field. Saeedvand has pioneered novel hybrid metaheuristic algorithms for multi-humanoid task allocation (43 citations) and hierarchical DRL frameworks that enable adult-sized humanoid robots to drag heavy objects (29 citations). His recent contributions include crowd-aware navigation systems using DRL (25 citations) and controlling a two-wheeled scooter with a humanoid robot (25 citations), demonstrating practical applications of his algorithms. In 2024, he published a comprehensive review of mobile robot navigation using DRL in crowded environments (38 citations), further solidifying his expertise. Notably, Saeedvand led the mechatronic design of the ARC humanoid robot—the first fully 3D-printed kid-sized open platform—showcasing his commitment to accessible, robust hardware. With over 330 total citations, his work bridges theoretical advances in multi-objective optimization and cooperative multi-robot control (15 citations) with tangible robotic systems, making him a key figure in advancing autonomous, humanoid-capable technologies.
Research Focus
Key Achievements
Top Papers
- 1A comprehensive survey on humanoid robot development106 citations · 2019
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- 9Novel lightweight odometric learning method for humanoid robot localization15 citations · 2018
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