Zeyuan Zheng
Papers
1
Total Citations
9
H-Index
1
About
Zeyuan Zheng is a pioneering researcher in human-robot interaction, with a focus on natural teaching paradigms and cross-modal perception for humanoid robotics. His most-cited work, "Natural teaching for humanoid robot via human-in-the-loop scene-motion cross-modal perception" (2019, 9 citations), introduces a groundbreaking approach to robot teleoperation by integrating scene and motion data through a human-in-the-loop framework. This paradigm enables more intuitive, efficient manipulation of life-sized humanoid robots, bridging the gap between human intent and robotic action. Zheng’s contributions lie in advancing cross-modal perception—where visual and motion cues are fused to enhance robot learning and control—addressing key challenges in dexterous manipulation and real-time teleoperation. His work has implications for assistive robotics, manufacturing, and remote operation in hazardous environments. By prioritizing natural, human-centric teaching methods, Zheng is shaping the future of intuitive human-robot collaboration, making complex robotic systems more accessible and responsive to human guidance.
Research Focus
Key Achievements
Top Papers
- 1