Zhinan Jiang
Papers
1
Total Citations
2
H-Index
1
About
Zhinan Jiang is a robotics researcher whose work focuses on bridging the gap between human gesture recognition and real-time robotic control, with a particular emphasis on hand keypoint detection for grasping tasks. In their most-cited work, "Optimized YOLO-Based Model for Real-Time Hand Keypoint Detection in Robotics" (2024), Jiang tackles a critical challenge in human-robot interaction: the computational limitations of embedded devices that prevent existing detection models from operating in real time. By optimizing the YOLO architecture, Jiang’s model enables robots to accurately and swiftly interpret human hand gestures, a foundational capability for intuitive robotic learning and manipulation. This contribution addresses a key bottleneck in deploying gesture-driven robotic systems in real-world settings, where speed and efficiency are paramount. With 2 citations already, this early-career work signals growing interest in Jiang’s practical, performance-oriented approach. Their research sits at the intersection of computer vision, embedded systems, and robotics, offering scalable solutions that make human-robot collaboration more seamless and responsive.
Research Focus
Key Achievements
Top Papers
- 1