Zahra Jamshidi
Papers
1
Total Citations
5
H-Index
1
About
Zahra Jamshidi is a researcher in robotics and artificial intelligence, with a primary focus on humanoid robot locomotion and adaptive control systems. Her most cited work, "A Shooting Strategy When Moving on Humanoid Robots Using Inverse Kinematics and Q-Learning" (2021), introduces a novel framework that combines inverse kinematics with reinforcement learning to enable more natural and efficient movement in humanoid robots. This approach allows robots to dynamically adjust their gait and shooting motions in real time, addressing key challenges in balance and coordination. With 5 citations, this paper has contributed to advancing the integration of machine learning techniques in robotic motion planning. Jamshidi’s research sits at the intersection of robotics, control theory, and AI, offering practical solutions for improving the agility and autonomy of humanoid platforms. Her work is particularly relevant for applications in search-and-rescue, assistive robotics, and human-robot interaction, where adaptive movement is critical. By bridging classical kinematics with modern reinforcement learning, Jamshidi is helping to shape the next generation of more responsive and capable humanoid robots.
Research Focus
Key Achievements
Top Papers
- 1