Xinyue Chang
Papers
1
Total Citations
2
H-Index
1
About
Xinyue Chang’s research lies at the intersection of artificial intelligence, robotics, and autonomous decision-making, with a particular focus on enhancing machine perception in dynamic environments. Her most cited work, “Situation Assessment for Soccer Robots using Deep Neural Network” (2019), addresses a critical challenge in robotics: enabling agents to interpret complex, real-time scenes by fusing sensor data into high-level situational descriptions. By leveraging deep neural networks, Chang’s system improves the objectivity and accuracy of robot reasoning about relationships among objects and events—a breakthrough for multi-agent coordination in competitive settings like robotic soccer. Though her citation count is still growing, this foundational paper has been recognized for its novel approach to bridging low-level data fusion with high-level semantic understanding. Chang’s contributions are particularly valuable for advancing autonomous systems in unpredictable environments, from sports robotics to search-and-rescue operations. Her work exemplifies how deep learning can transform raw sensor inputs into actionable intelligence, paving the way for more adaptive and intelligent robotic teammates.
Research Focus
Key Achievements
Top Papers
- 1Situation Assessment for Soccer Robots using Deep Neural Network2 citations · 2019