Qiushi Zhang
Papers
1
Total Citations
4
H-Index
1
About
Qiushi Zhang is a rising researcher in embodied AI and human-robot interaction, with a focus on enabling robots to anticipate human intent from egocentric vision. His most cited work, "EgoPAT3Dv2: Predicting 3D Action Target from 2D Egocentric Vision for Human-Robot Interaction" (2024), tackles the critical challenge of predicting the 3D spatial target of a hand’s movement from first-person video—a capability essential for safe and efficient collaboration between humans and robots. This work advances beyond prior efforts limited to semantic action classification or 2D region prediction, introducing a framework that bridges 2D visual cues to 3D action targets. With 4 citations in its first year, the paper signals growing interest in his approach. Zhang’s contributions lie at the intersection of computer vision, robotics, and cognitive science, aiming to make HRI more intuitive and responsive. His research has implications for assistive robotics, manufacturing, and autonomous systems, where anticipating human motion in three dimensions is key. As an early-career scholar, Zhang is establishing a reputation for tackling underexplored problems in egocentric perception, and his work is poised to influence how robots understand and react to human actions in real-world environments.
Research Focus
Key Achievements
Top Papers
- 1