Papers
10
Total Citations
41
H-Index
3
About
Shuaihang Yuan is a researcher at the forefront of embodied AI, assistive robotics, and 3D scene understanding. His work centers on developing intelligent systems that can perceive, reason about, and safely interact with complex environments—with a particular emphasis on zero-shot generalization and human-centered applications. Yuan’s most impactful contribution is the development of a multi-modal foundation model to assist people with blindness and low vision (pBLV), which has already garnered 16 citations since its 2024 publication. This work addresses critical challenges in comprehensive scene recognition and hazard identification for visually impaired users. He has also pioneered the exploration of security vulnerabilities in LLM-based navigation systems, a timely contribution as these systems become more prevalent in urban environments. Yuan’s research on zero-shot object goal navigation, leveraging vision-language models and geometric-affordance guidance, has produced a series of highly cited papers (2024) that advance the reliability and semantic understanding of autonomous agents. His recent work on MultiTalk introduces introspective and extrospective dialogue frameworks for aligning human intent, environmental constraints, and LLM reasoning. With multiple publications in 2024 alone, Yuan is rapidly establishing himself as a leading voice in building trustworthy, accessible, and generalizable AI systems for real-world deployment.
Research Focus
Key Achievements
Top Papers
- 1
- 2Zero-Shot Object Navigation with Vision-Language Models Reasoning9 citations · 2024
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- 7A single 3D shape wavelet-based generative model2 citations · 2024
- 8Noisy Few-shot 3D Point Cloud Scene Segmentation2 citations · 2024
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- 10Goal-Driven Transformer for Robot Behavior Learning from Play Data1 citations · 2024