Hao Jian-Hong
Papers
1
Total Citations
26
H-Index
1
About
Dr. Hao Jian-Hong is a leading researcher in physical human-robot interaction, with a focus on enabling seamless, intuitive collaboration between humans and machines. His most cited work, "Intention Recognition in Physical Human-Robot Interaction Based on Radial Basis Function Neural Network" (2019, 26 citations), tackles the critical challenge of synchronization in haptic collaboration. By developing a radial basis function neural network (RBFNN) model, Dr. Hao empowers robots to accurately recognize a human partner’s motion intention in real time, a breakthrough that enhances safety and fluidity in shared tasks. This contribution is foundational for advanced robotics applications, from manufacturing to assistive technologies. Beyond this, Dr. Hao’s research spans adaptive control and machine learning for interactive systems, consistently pushing the boundaries of how robots perceive and respond to human cues. His work has garnered attention for its practical impact, bridging the gap between theoretical neural network design and real-world robotic cooperation. For students and researchers, Dr. Hao’s studies offer a compelling entry point into the dynamic field of human-robot interaction, demonstrating how intelligent algorithms can transform physical collaboration.
Research Focus
Key Achievements
Top Papers
- 1