Papers
37
Total Citations
1,109
H-Index
19
About
Minho Hwang is a robotics and artificial intelligence researcher whose work spans surgical robotics, safe reinforcement learning, and robotic manipulation. His research sits at the intersection of machine learning and physical robotic systems, with a particular emphasis on making autonomous robots both capable and safe in real-world environments. Hwang's contributions to surgical robotics have been especially notable. His development of the K-FLEX flexible robotic platform and continuum manipulators has advanced minimally invasive surgery by enabling greater accessibility and agility in confined anatomical spaces. His work on gravity compensation mechanisms and path planning for surgical robots further demonstrates his systems-level thinking in medical applications. Complementing this hardware expertise, Hwang pioneered deep learning approaches to surgical task automation — including peg transfer, depth-sensing-guided manipulation, and visual servoing — achieving performance rivaling or exceeding human surgeons. Beyond surgery, Hwang has made significant contributions to fabric manipulation using deep imitation learning and simulation-to-real transfer, with applications ranging from manufacturing to household robotics. His Recovery RL framework, addressing the fundamental tension between exploration and safety in reinforcement learning, has garnered 193 citations and stands as his most impactful theoretical contribution. With over 760 cumulative citations across his top publications, Hwang represents a versatile and highly influential voice in modern robotics research.
Research Focus
Key Achievements
Top Papers
- 1Recovery RL: Safe Reinforcement Learning With Learned Recovery Zones193 citations · 2021
- 2K‐FLEX: A flexible robotic platform for scar‐free endoscopic surgery124 citations · 2020
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- 4Strong Continuum Manipulator for Flexible Endoscopic Surgery86 citations · 2019
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