Adnan Munawar
Papers
26
Total Citations
372
H-Index
11
About
Adnan Munawar is a pioneering researcher at the intersection of surgical robotics, simulation, and autonomous control systems. His work focuses on three tightly interwoven areas: real-time robot simulation, robot-assisted minimally invasive surgery, and machine learning-driven surgical autonomy. Munawar's most influential contribution is the development of the Asynchronous Multi-Body Framework (AMBF), a dynamic simulation environment capable of modeling complex robotic systems that challenge conventional simulators — a paper that has garnered 59 citations and underpins much of his subsequent work. Building on this foundation, he has advanced human-robot shared control frameworks, reinforcement learning for surgical task automation such as suturing hand-off (46 citations), and semi-autonomous surgical control, collectively accumulating over 300 citations across his portfolio. His open-source contributions, including the Collaborative Robotics Toolkit (CRTK) and AMBF-RL, reflect a strong commitment to reproducible, community-driven research. Munawar has also made notable strides in haptic feedback integration for laparoscopic platforms. For students and researchers entering surgical robotics, his body of work offers both foundational simulation infrastructure and cutting-edge autonomy solutions, making him a central figure in the field's rapid evolution.
Research Focus
Key Achievements
Top Papers
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