Jianbing Hu

About

Jianbing Hu is a leading researcher in robot learning and control, with a particular focus on dynamical systems and human-robot interaction. His most influential work, "Fast and Stable Learning of Dynamical Systems Based on Extreme Learning Machine" (2017, 71 citations), addresses a critical challenge in robotics: enabling robots to learn complex motions from human demonstrations with high accuracy, stability, and speed. Hu pioneered the use of Extreme Learning Machines (ELM) to model motions as autonomous dynamical systems, establishing sufficient conditions for global stability at the target—a breakthrough that ensures safe and reliable robot movement. His earlier work, "A Real-Time Human Imitation System Using Kinect" (2015, 51 citations), further demonstrates his expertise in bridging human demonstration and robotic execution. Beyond learning algorithms, Hu has contributed to mechanical design, notably developing a wheel-track transformation robot for search and rescue missions, and exploring shared control methods for hazardous environments like coal mines. With over 140 total citations, Hu’s research continues to shape the fields of robot motion planning, imitation learning, and human-robot collaboration, making him a key figure in advancing intelligent, adaptable robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Fast and Stable Learning of Dynamical Systems Based on Extreme Learning Machine

   71 citations · 2017
2. 2
   A Real-Time Human Imitation System Using Kinect

   51 citations · 2015
3. 3
   Neural learning of stable dynamical systems based on extreme learning machine

   12 citations · 2015
4. 4
   On study of a wheel-track transformation robot

   7 citations · 2015
5. 5
   Shared Control Method for Coal Mine Rescue Robots

   3 citations · 2021
6. 6
   Mechanical design for wheel/track transform mobile platform-search and rescue robot

   2 citations · 2014

Key Collaborators

Contact & Links