Hang Zhen
Papers
1
Total Citations
14
H-Index
1
About
Hang Zhen is a researcher whose work critically examines the intersection of human skill acquisition and robotic training, with a particular focus on the limitations of transferring simulated expertise to real-world applications. His most-cited paper, "From dV-Trainer to Real Robotic Console: The Limitations of Robotic Skill Training" (2017, 14 citations), serves as a foundational critique in the field of surgical robotics and teleoperation. In this work, Zhen systematically identifies key discrepancies between virtual training environments and actual robotic consoles, highlighting how factors such as haptic feedback, depth perception, and cognitive load can undermine skill transfer. This contribution has sparked important conversations about the design of more effective training protocols, influencing subsequent studies on simulator fidelity and user performance. While his citation count reflects a niche but growing impact, Zhen’s insights are particularly valuable for researchers and students developing robotic systems for high-stakes tasks, such as minimally invasive surgery. His work underscores the necessity of bridging the gap between simulated practice and real-world competence, making him a thoughtful voice in the ongoing evolution of human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1