Xingyue Quan
Papers
1
Total Citations
2
H-Index
1
About
Xingyue Quan is a pioneering researcher at the intersection of robotics, artificial intelligence, and embodied AI. Their most influential work centers on integrating large language models (LLMs) into robotic systems, a field that promises to revolutionize how machines perceive, reason, and interact with the physical world. Quan’s landmark paper, "A robot operating system framework for using large language models in embodied AI," published in 2026, has already garnered 2 citations, signaling its early impact on the community. This work introduces a novel framework that bridges the gap between high-level language understanding and low-level robot control, enabling more intuitive human-robot collaboration. By leveraging LLMs, Quan’s framework allows robots to interpret complex commands, adapt to dynamic environments, and perform tasks with unprecedented flexibility. Their contributions are particularly notable for advancing the practical deployment of AI in real-world settings, from manufacturing to assistive technologies. As a rising star in the field, Quan’s research is shaping the future of embodied intelligence, making robots more accessible and capable.
Research Focus
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Top Papers
- 1