Cheng Chang Lu
Papers
1
Total Citations
3
H-Index
1
About
Cheng Chang Lu is a researcher at the intersection of human-robot interaction, computer vision, and formal modeling, with a focus on enabling more natural communication between humans and social robots. His most notable contribution is the development of a Synchronous Colored Petri Net-based framework for modeling and analyzing conversational head-gestures, published in 2021. This work provides a rigorous, formal method to capture the timing and sequence of non-verbal cues—such as nods and head tilts—during dialogue, which is critical for training robots to interpret and respond to human social signals. By integrating Petri net theory with video analysis, Lu offers a systematic approach to decompose complex gesture patterns, bridging the gap between low-level motion data and high-level conversational intent. While his work is still emerging, with his key paper accumulating 3 citations, it represents a foundational step toward more perceptive and socially adept robotic systems. Lu’s research is particularly valuable for students and engineers in human-robot interaction, as it provides a structured methodology for teaching robots the subtle, often overlooked language of the body.
Research Focus
Key Achievements
Top Papers
- 1