Papers
28
Total Citations
601
H-Index
13
About
Xiao Liang is a pioneering researcher at the intersection of human–robot collaboration (HRC), intelligent disassembly, and human motion prediction, with a growing body of work that is reshaping how end-of-life products are recovered and remanufactured. His research addresses one of modern manufacturing's most pressing challenges: automating the complex, uncertain, and labor-intensive process of product disassembly for recycling and remanufacturing applications. Liang's most influential contribution, "Task Allocation and Planning for Product Disassembly with Human–Robot Collaboration" (2022, 161 citations), established foundational frameworks for coordinating humans and robots in disassembly workflows. Building on this, he has developed sophisticated algorithms for real-time human motion prediction—including transformer-based and Kalman filter approaches—that enable robots to anticipate human behavior and respond safely and efficiently. His 2024 work, TransFusion, advances 3D human motion prediction using diffusion models, reflecting his commitment to pushing the boundaries of AI in collaborative robotics. With over 490 cumulative citations across his top publications and a comprehensive review paper synthesizing opportunities in HRC disassembly, Liang has become a leading voice in sustainable manufacturing automation, providing both theoretical rigor and practical tools for the intelligent factories of tomorrow.
Research Focus
Key Achievements
Top Papers
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- 8Uncertainty-Assisted Image-Processing for Human-Robot Close Collaboration24 citations · 2022
- 9Disassembly Sequence Planning Considering Human-Robot Collaboration23 citations · 2020
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