Papers
21
Total Citations
862
H-Index
15
About
Minhan Li is a robotics and rehabilitation engineering researcher whose work spans two interconnected domains: continuum robotics and intelligent wearable robotic systems for human mobility assistance. His early contributions to continuum robots — particularly his model-free adaptive Kalman filter control framework (189 citations) and tendon-driven continuum robot design (115 citations) — established practical solutions for controlling structurally compliant robots operating in uncertain environments, addressing longstanding challenges in kinematic modeling and controller robustness. Li has since become a prominent figure in reinforcement learning-driven personalization of prosthetic and exoskeletal devices. His research tackles the critical challenge of automatically tuning robotic prosthesis parameters to individual users, demonstrating how reinforcement learning and human-in-the-loop optimization can replace laborious manual calibration. Works on knee prosthesis impedance tuning (80 citations), vision-based environmental context prediction (66 citations), and hip exoskeleton personalization collectively reflect his commitment to making wearable robots clinically practical and adaptive. Across more than ten highly cited publications, Li's research has garnered over 700 citations, underscoring his significant influence on the fields of medical robotics, adaptive control, and human-robot interaction — with profound implications for improving the quality of life for individuals with mobility impairments.
Research Focus
Key Achievements
Top Papers
- 1Model-Free Control for Continuum Robots Based on an Adaptive Kalman Filter189 citations · 2017
- 2Design and control of a tendon-driven continuum robot115 citations · 2017
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