Yunfeng Wu
Papers
1
Total Citations
23
H-Index
1
About
Yunfeng Wu is a leading researcher in embodied AI and robot learning, with a particular focus on vision-language-action models (VLAs) and their application to generalizable sensorimotor control. His most influential work, "CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models" (2025, 23 citations), introduces a novel framework that enhances VLAs by integrating visual chain-of-thought reasoning, enabling robots to decompose complex tasks into interpretable, step-by-step visual and action sequences. This contribution addresses a critical bottleneck in robotics—bridging the gap between large-scale pretrained vision-language models and real-world robotic control—by improving both reasoning transparency and task generalization. Wu’s research has been recognized for its impact on advancing data-efficient robot learning, leveraging diverse demonstration sources to achieve robust performance across varied environments. With a growing citation record, his work is shaping the next generation of intelligent robotic systems, making him a key figure in the intersection of computer vision, natural language processing, and robotics.
Research Focus
Key Achievements
Top Papers
- 1CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models23 citations · 2025