Hamid Bamshad
Papers
3
Total Citations
9
H-Index
2
About
Hamid Bamshad is a robotics researcher whose work lies at the intersection of tactile sensing, intelligent actuation, and autonomous learning. His primary research areas include slip detection for robotic grippers, compact electrohydraulic actuation, and reinforcement learning for robotic agents. Bamshad’s most notable contribution is a high-performance multilayer-perceptron-based slip detection algorithm, which uses only normal force data from tactile sensors to enable autonomous grasping—a critical capability for modern robotics. This work has already garnered 5 citations since its 2023 publication, signaling growing interest in practical, sensor-driven manipulation. He has also advanced the field of actuation by comparing genetic programming with dynamic models for compact electrohydraulic actuators, offering insights into efficient, space-saving hydraulic systems for automation and aerospace. Additionally, his 2020 study on reinforcement learning with converging goal spaces and binary reward functions tackles the challenge of sparse rewards in vast environments, helping robotic agents learn more effectively. Through these contributions, Bamshad demonstrates a clear commitment to making robots more dexterous, efficient, and autonomous—work that holds promise for both industrial applications and foundational robotics research.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3