Papers
5
Total Citations
36
H-Index
3
About
Guangyao Shi is an emerging researcher at the intersection of artificial intelligence, robotics, and computer vision, with a focus on multimodal learning, autonomous systems, and robot-assisted applications. His most-cited work, a comprehensive survey on Large Vision Language Models (2025, 18 citations), establishes him as a thoughtful synthesizer of cutting-edge developments in multimodal AI, examining how models like CLIP bridge visual and textual understanding. His work on LAVA (2024, 8 citations) demonstrates a strong applied dimension, advancing robotic-assisted feeding systems capable of handling complex, liquid-rich foods — a meaningful contribution to assistive technology for individuals with mobility impairments. Shi also investigates multi-robot coordination, as seen in his risk-aware resource allocation framework for UAV-UGV recharging rendezvous (2022), and pushes the boundaries of robust control theory through data-driven distributionally robust methods under state-dependent uncertainty (2023). His exploration of Large Language Models for robot routing further reflects his commitment to integrating generative AI into real-world autonomous systems. Across these diverse yet interconnected domains, Shi consistently bridges theoretical rigor with practical impact, making his work valuable reading for researchers in robotics, AI, and human-assistive technologies.
Research Focus
Key Achievements
Top Papers
- 1
- 2LAVA: Long-horizon Visual Action based Food Acquisition8 citations · 2024
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- 5Can Large Language Models Solve Robot Routing?3 citations · 2024