Linlin Cheng
Papers
1
Total Citations
2
H-Index
1
About
Linlin Cheng is an emerging researcher specializing in human-robot interaction (HRI) and computer vision, with a particular focus on gaze estimation technologies. Their most notable work investigates the application of appearance-based gaze estimation methods within social robotics contexts, addressing a critical challenge in the field: enabling robots to naturally perceive and interpret human visual attention without requiring cumbersome external devices or calibration procedures. Cheng's research systematically evaluates state-of-the-art gaze estimation models by identifying and characterizing the boundary conditions under which these methods perform reliably in real-world HRI scenarios. This contribution is especially significant as it bridges the gap between laboratory-grade gaze estimation performance and practical deployment on social robotic platforms, providing the research community with grounded benchmarks and insights for future system development. Although early in their academic career — with their 2023 publication accumulating 2 citations — Cheng's work addresses a timely and increasingly relevant problem as socially intelligent robots become more prevalent in everyday environments. Their research lays important groundwork for developers and scientists seeking to implement robust, device-free gaze sensing capabilities in next-generation human-robot interaction systems.
Research Focus
Key Achievements
Top Papers
- 1