Haiyang Fang

About

Haiyang Fang is a pioneering researcher at the intersection of reinforcement learning, robotics, and medical device innovation. His work primarily focuses on adaptive control systems and surgical robotics, with significant contributions to both theoretical frameworks and practical applications. Fang’s most cited paper (45 citations) introduces a fuzzy-based adaptive optimization method for nonlinear Markov jump systems using off-policy reinforcement learning, advancing control theory for complex dynamic environments. In surgical robotics, he developed a cross-entropy motion planning framework for hybrid continuum robots (14 citations), enabling efficient navigation in constrained anatomical spaces. His notable achievements include designing an MR-safe robotic manipulator with hydraulic bellows actuators for spine procedures (9 citations), addressing critical safety and precision challenges in MRI-guided lumbar injections. Additionally, Fang engineered a flexible sensorized robotic OCT neuroendoscope (5 citations), enhancing endoscopic imaging capabilities for microstructural visualization. His work demonstrates a rare ability to bridge theoretical control algorithms with tangible medical technologies, impacting fields from adaptive optimization to minimally invasive surgery. With a growing citation record, Fang’s research continues to shape the future of intelligent robotic systems in healthcare.

Research Focus

Key Achievements

Top Papers

1. 1
   Fuzzy-Based Adaptive Optimization of Unknown Discrete-Time Nonlinear Markov Jump Systems With Off-Policy Reinforcement Learning

   45 citations · 2022
2. 2
   A Cross-Entropy Motion Planning Framework for Hybrid Continuum Robots

   14 citations · 2023
3. 3
   An MR-Safe Robotic Manipulator With Hydraulic Bellows Actuators for Spine Procedures

   9 citations · 2023
4. 4
   Design and Evaluation of a Flexible Sensorized Robotic OCT Neuroendoscope

   5 citations · 2023

Key Collaborators

Contact & Links