Papers
7
Total Citations
112
H-Index
4
About
Xiaoyun Liang is at the forefront of integrating robotics and artificial intelligence into the construction industry, with a primary focus on human-robot collaboration (HRC). Their research addresses critical challenges in dynamic construction environments, including safe path planning, worker intention prediction, and ergonomic workload assessment. Liang’s most influential work, “Prediction-Based Path Planning for Safe and Efficient Human–Robot Collaboration in Construction via Deep Reinforcement Learning” (74 citations), pioneers the use of deep reinforcement learning to enable robots to navigate complex workspaces while ensuring worker safety. They have further advanced the field by developing multi-task deep learning models for human intention prediction and robot-aware 3D motion forecasting, as seen in their 2025 publication on agentic AI-empowered HRC. Liang’s experimental analyses, such as the 2024 study on collaborative wood assembly (16 citations), provide empirical evidence of how collaborative robots impact work performance and worker perception. Their innovative motion-based control interface for teleoperation also demonstrates a commitment to intuitive, hands-free robot control. With a growing citation record and contributions spanning from visual attention analysis to workload assessment, Liang is shaping a future where construction robots are not just tools, but intelligent, context-aware partners.
Research Focus
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