Mohamad Roshanzamir
Papers
1
Total Citations
5
H-Index
1
About
Mohamad Roshanzamir is a researcher whose work lies at the intersection of reinforcement learning and autonomous robotics, with a particular focus on humanoid robot control and multi-agent systems. His most cited work, "A reinforcement learning approach to score goals in RoboCup 3D soccer simulation for NAO humanoid robot" (2017, 5 citations), demonstrates a pioneering application of reinforcement learning to teach robots complex, dynamic behaviors without requiring hard-coded instructions. By enabling NAO humanoid robots to learn optimal decision-making for scoring goals in the competitive RoboCup environment, Roshanzamir has contributed to advancing the state of the art in robot autonomy and skill acquisition. His research shows how reinforcement learning can replace detailed programming for tasks that involve real-time adaptation and coordination, a key challenge in robotics. This work not only has implications for sports robotics but also for broader applications in autonomous systems where robots must learn from interaction with their environment. Roshanzamir’s contributions help bridge the gap between theoretical reinforcement learning algorithms and practical, embodied robotic performance.
Research Focus
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Top Papers
- 1