Mohammad Aman Ullah Al Amin
Papers
1
Total Citations
24
H-Index
1
About
Mohammad Aman Ullah Al Amin is a pioneering researcher at the intersection of robotics, digital twin technology, and human-robot collaboration. His work focuses on developing intelligent control frameworks that bridge the gap between human intent and robotic action, particularly through imitation learning and teleoperation. His most-cited paper, "Control framework for collaborative robot using imitation learning-based teleoperation from human digital twin to robot digital twin" (2022), has garnered 24 citations, establishing a foundational approach for enabling robots to learn complex tasks by observing human digital twins. This contribution is pivotal for advancing collaborative robotics in manufacturing and healthcare, where safe, intuitive human-robot interaction is critical. Al Amin’s research not only enhances robotic autonomy but also reduces the programming burden, making robots more accessible to non-experts. His work has been recognized for its potential to transform industrial automation, and he continues to explore how digital twins can serve as a bridge for seamless skill transfer between humans and machines. With a growing citation impact, Al Amin is a rising voice in the field of cyber-physical systems and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1