Papers
3
Total Citations
35
H-Index
2
About
Longwu Yan is a rising researcher at the intersection of robotics and artificial intelligence, with a focused expertise in enabling robots to autonomously perform adaptive tasks. His primary contributions lie in integrating Large Language Models (LLMs) with Behavior Trees (BTs) to overcome a critical challenge in robotics: handling external disturbances during task execution. Yan’s most influential work, “LLM-BT: Performing Robotic Adaptive Tasks based on Large Language Models and Behavior Trees” (2024), has garnered 30 citations, demonstrating its immediate impact on the field. In this seminal paper, he proposes a novel framework that leverages ChatGPT to parse human instructions and dynamically generate or modify behavior trees, allowing robots to adapt in real-time to unexpected changes in their environment. This approach bridges the gap between high-level language understanding and low-level robotic control, offering a practical solution for more resilient and intelligent automation. Yan’s ongoing research, including a 2025 follow-up study, continues to refine these methods, promising to advance the capabilities of service and industrial robots. His work is particularly notable for making complex robotic adaptation more accessible through natural language, positioning him as a key contributor to the next generation of human-robot interaction.
Research Focus
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