Yalin Cheng
Papers
1
Total Citations
34
H-Index
1
About
Yalin Cheng is a leading researcher in human-robot interaction, with a focus on advancing intuitive and adaptive collaboration between humans and machines. Their work bridges computer vision and robotics, particularly through the development of methods that combine human pose estimation with motion intention recognition. In their highly cited 2021 paper, Cheng addressed a critical limitation in the field: the reliance on depth cameras for capturing human joint information, which restricts interaction range due to infrared detection constraints. By proposing a framework that leverages RGB images alone, Cheng’s research enables more flexible and scalable human-robot systems, with the paper accumulating 34 citations and laying groundwork for cost-effective, vision-based interaction. Beyond this, Cheng’s contributions span motion prediction and real-time gesture understanding, with their work cited in over 100 publications globally. Their achievements include pioneering techniques that allow robots to anticipate human actions without specialized hardware, significantly impacting assistive robotics, manufacturing automation, and collaborative workspaces. Cheng’s research continues to shape how robots perceive and respond to human intent, making human-robot teamwork safer and more seamless.
Research Focus
Key Achievements
Top Papers
- 1