Yunde Jiar

Beijing Institute of Technology, Carnegie Mellon University

Papers

Total Citations

H-Index

About

Yunde Jiar is a pioneer in robotic manipulation and human-robot interaction, with a research focus on tactile sensing and vision-based robot programming. His most-cited work introduces a high-resolution, high-compliance tactile sensing system that leverages optical reflection and clear rubber’s mechanical compliance to achieve both precision and adaptability in robotic grippers—a critical advance for dexterous manipulation tasks. Complementing this, Jiar’s research on hand action perception enables automatic robot programming from depth image sequences, allowing human instructors to demonstrate assembly tasks without datagloves or markers. This vision-based approach, detailed in his 1999 and 2002 papers, simplifies robot instruction by mimicking natural human teaching methods, where a teacher demonstrates and a student replicates. With over 17 combined citations, his contributions bridge tactile feedback and intuitive programming, laying groundwork for more autonomous, human-friendly robotics. Jiar’s work stands out for its practical impact on manufacturing and assistive technologies, offering a seamless path from human demonstration to robotic execution.