Kamal Jamshidi
Papers
8
Total Citations
61
H-Index
5
About
Kamal Jamshidi is a leading researcher in biologically inspired robotics, with a primary focus on locomotion and learning in humanoid robots. His work is centered on developing neural control architectures, particularly Central Pattern Generators (CPGs), to enable stable and adaptive walking in platforms like the Nao humanoid robot. A key contribution is his 2013 paper on biologically inspired layered learning, which has garnered 19 citations, establishing a framework for hierarchical skill acquisition. Jamshidi pioneered the use of policy gradient methods for curvilinear bipedal walking, as detailed in his 2011 work (9 citations), and advanced the field by modeling the mesencephalic locomotor region for robotic control (8 citations). He has also introduced innovative training techniques, such as natural gradient particle swarm optimization for oscillatory neural networks (7 citations), and explored sensor-based CPG programming (5 citations) and imitation learning (8 citations). His research bridges computational neuroscience and practical robotics, offering students a compelling example of how biological principles can solve complex engineering challenges in autonomous locomotion.
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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