Yifang Gao
Papers
2
Total Citations
13
H-Index
2
About
Yifang Gao is a rising researcher at the forefront of integrating large language models (LLMs) into real-time robotic systems. Their primary research areas span human-robot interaction (HRI), real-time computer vision, and LLM-driven robotic inference. Gao’s most notable contribution is the development of **LAMARS** (Large Language Model-Based Anticipation Mechanism Acceleration in Real-Time Robotic Systems), a pioneering framework that addresses the critical latency bottleneck of deploying LLMs in dynamic robotic environments. This work, which has already garnered 7 citations since its 2024 publication, demonstrates how LLMs can be effectively leveraged for rapid task handling and inference without sacrificing real-time performance. Complementing this, Gao conducted a comprehensive benchmarking study (2025, 6 citations) evaluating YOLOv8 through YOLOv13 models for robust hand gesture recognition in HRI, providing the field with a crucial performance roadmap for selecting optimal detection architectures. By bridging the gap between powerful but computationally heavy LLMs and the strict latency demands of physical robots, Gao is helping to unlock more intuitive, safe, and responsive autonomous systems. Their work is essential reading for anyone interested in the practical deployment of foundation models in robotics.
Research Focus
Key Achievements
Top Papers
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