Xiongjie Zhang
Papers
1
Total Citations
24
H-Index
1
About
Xiongjie Zhang is a leading researcher in human-robot collaboration, with a primary focus on intention recognition and adaptive robotic control. His most-cited work, "Human-Robot Collaboration by Intention Recognition using Deep LSTM Neural Network" (2019, 24 citations), introduces a novel framework that enables robots to interpret human motion sequences in real time, allowing for more fluid and intuitive teamwork. By leveraging deep LSTM neural networks, Zhang’s approach allows robots to predict a human partner’s next action based on skeletal motion data, significantly improving efficiency and safety in shared workspaces. This contribution has been foundational for researchers exploring non-verbal communication in collaborative robotics, bridging the gap between raw sensor data and meaningful task-level understanding. Zhang’s work is particularly notable for its practical implications in manufacturing and assistive robotics, where seamless human-robot interaction is critical. His research continues to influence the development of intelligent robotic systems that can anticipate human needs, marking him as a key figure in advancing collaborative autonomy.
Research Focus
Key Achievements
Top Papers
- 1