Hamed Shahbazi
Papers
10
Total Citations
65
H-Index
5
About
Hamed Shahbazi’s research lies at the intersection of biologically inspired robotics, neural control, and machine learning, with a focus on enabling humanoid robots to achieve dynamic, adaptive locomotion. His major contributions center on the use of Central Pattern Generators (CPGs)—neural circuits inspired by vertebrate locomotion—to program and learn complex bipedal walking patterns. In his most cited work, "Biologically inspired layered learning in humanoid robots" (19 citations), he introduced a hierarchical framework that decomposes walking into manageable sub-tasks, allowing robots like the Nao humanoid to learn curvilinear paths and stable gaits. His 2011 paper on "Curvilinear Bipedal Walk Learning" (9 citations) pioneered a policy gradient method for CPG-based walking, while subsequent studies on oscillatory neural networks and mesencephalic locomotor region modeling (8 citations each) deepened the biological plausibility of his control systems. Shahbazi’s work has practical impact in robotics education and soccer-playing humanoids, with his methods achieving over 50 total citations. His recent 2024 paper on robotic puppetry demonstrates ongoing innovation, extending CPG networks to artistic applications. For students and researchers, Shahbazi’s research offers a compelling blueprint for bridging neuroscience and engineering to create more lifelike, autonomous robots.
Research Focus
Key Achievements
Top Papers
- 1Biologically inspired layered learning in humanoid robots19 citations · 2013
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- 4Modeling of mesencephalic locomotor region for Nao humanoid robot8 citations · 2012
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