Yixuan Sheng
Papers
2
Total Citations
51
H-Index
2
About
Yixuan Sheng is a leading researcher in human-robot collaboration (HRC), with a focus on enabling safer and more intuitive interactions between humans and robotic systems. Their key research areas include motion prediction, human intention estimation, and tactile sensing for collaborative robotics. Sheng’s most impactful work, "Optimal Collision-Free Robot Trajectory Generation Based on Time Series Prediction of Human Motion" (2017, 48 citations), introduced a novel approach using autoregressive models to predict human joint motion during repetitive tasks, allowing robots to plan collision-free trajectories in real time. This work is foundational for adaptive HRC in manufacturing and assistive settings. In a complementary study, "Human Intention Estimation With Tactile Sensors in Human-Robot Collaboration" (2017), Sheng applied machine learning to interpret force distribution changes on interaction surfaces, enabling robots to infer human intentions during physical cooperation. Together, these contributions advance the field by merging predictive modeling with sensor-based perception, enhancing both safety and efficiency in shared workspaces. Sheng’s research is particularly notable for its practical applications in industrial automation and human-centered robotics.
Research Focus
Key Achievements
Top Papers
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