Papers
1
Total Citations
4
H-Index
1
About
Yi Xing is a researcher advancing the frontier of human-robot collaboration, with a core focus on motion prediction and physical human-robot interaction. Their most notable contribution is the development of a long short-term human motion prediction method using Long Short-Term Memory (LSTM) networks, specifically designed for human-robot co-carrying tasks. This work, published in 2023, addresses a critical challenge: enabling robots to anticipate human motion targets over extended time horizons. By accurately predicting where a human partner intends to move, the robot can proactively lead the task rather than merely react, enhancing coordination, safety, and efficiency in shared physical tasks. While the paper has garnered 4 citations to date, its impact lies in its practical approach to a fundamental problem in collaborative robotics. Xing’s research bridges the gap between theoretical motion modeling and real-world applications, offering a pathway toward more intuitive and responsive robotic assistants. This work is particularly relevant for students and researchers interested in human-robot interaction, machine learning for robotics, and the future of autonomous systems working alongside humans.
Research Focus
Key Achievements
Top Papers
- 1Long Short-Term Human Motion Prediction in Human-Robot Co-Carrying4 citations · 2023