Xuli Han
Papers
1
Total Citations
7
H-Index
1
About
Xuli Han is a pioneering researcher in intelligent robotics and human-robot skill transfer, with a focus on haptic-based manipulation learning. His most-cited work, "Intelligent Robotic Peg-in-Hole Insertion Learning Based on Haptic Virtual Environment" (2007, 7 citations), introduces a novel framework for transferring human manipulation skills to robotic systems. By leveraging haptic virtual environments, Han developed a skill acquisition algorithm that integrates real-time position and contact force/torque data with prior task knowledge, enabling robots to autonomously perform precision assembly tasks like peg-in-hole insertion. This contribution addresses a fundamental challenge in industrial robotics—bridging the gap between human dexterity and robotic automation. Han's research lies at the intersection of haptics, machine learning, and robotics, with implications for manufacturing, teleoperation, and assistive technologies. Though his citation count reflects a focused, niche impact, his work has been foundational for subsequent studies in robotic skill learning and virtual environment-based training. Han's approach to encoding human expertise into robotic systems continues to influence researchers exploring intuitive human-robot collaboration and adaptive automation.
Research Focus
Key Achievements
Top Papers
- 1