S. Yaser Khodkam
Papers
1
Total Citations
13
H-Index
1
About
S. Yaser Khodkam is a pioneering researcher at the intersection of robotics, neural computation, and intelligent control systems. His primary research areas include spiking neural networks (SNNs), hybrid learning control, and reinforcement learning for robotic applications. Khodkam’s most notable contribution is the development of a novel hybrid learning control system for robots, which integrates SNNs with reinforcement learning to create adaptive, multi-input multi-output controllers. This groundbreaking work, published in 2024, has already garnered 13 citations, signaling its rapid impact on the field. By replacing traditional nonlinear controllers with SNNs that can tune their parameters in real time through reinforcement learning, Khodkam has opened new pathways for more efficient, biologically inspired robotic autonomy. His approach addresses critical challenges in adaptive control, enabling robots to learn and adjust their behavior without explicit programming. This work is particularly significant for applications in autonomous navigation, manipulation, and human-robot interaction. Khodkam’s research continues to push the boundaries of how machines can learn from their environments, making him a rising figure in computational neuroscience and robotics.
Research Focus
Key Achievements
Top Papers
- 1