Sijie Wen
Papers
1
Total Citations
30
H-Index
1
About
Sijie Wen is a pioneering researcher at the intersection of artificial intelligence, robotics, and sustainable manufacturing. Their work focuses on developing intelligent systems for dynamic disassembly and human-robot collaboration, particularly in the context of circular economy and remanufacturing. Wen’s most notable contribution is the introduction of a novel framework that integrates multimodal large language models (MLLMs) with knowledge graphs (KGs) to enable real-time rescheduling of collaborative tasks under unpredictable disassembly scenarios. This breakthrough, detailed in their 2025 paper "Rescheduling human-robot collaboration tasks under dynamic disassembly scenarios: An MLLM-KG collaboratively enabled approach," has already garnered 30 citations, signaling its rapid influence in the field. By bridging natural language understanding with structured domain knowledge, Wen’s work addresses a critical gap in adaptive automation, allowing robots to reason about changing environments and coordinate seamlessly with human workers. Their research holds significant promise for improving efficiency and flexibility in e-waste recycling and end-of-life product processing. Wen’s innovative approach positions them as a rising leader in smart manufacturing and human-centered AI, with potential applications extending to logistics, healthcare, and beyond.
Research Focus
Key Achievements
Top Papers
- 1