Weili Guan
Papers
1
Total Citations
1
H-Index
1
About
Weili Guan is a leading researcher at the intersection of artificial intelligence and robotics, with a primary focus on advancing robotic manipulation through generative modeling. Her most notable contribution is the comprehensive survey "A Survey on Diffusion Policy for Robotic Manipulation: Taxonomy, Analysis, and Future Directions," which systematically maps the emerging use of diffusion models—originally developed for image generation—to learn effective control policies in complex, high-dimensional action spaces and uncertain environments. This work provides a critical taxonomy and analysis that helps researchers navigate the rapidly evolving field of diffusion-based policy learning. While her citation count is still growing, reflecting the recency of her work, Guan's survey has quickly become a foundational reference for researchers tackling the challenges of robotic dexterity and adaptability. Her research addresses fundamental obstacles in robotics, including the difficulty of learning robust policies in unpredictable settings. By bridging the gap between generative AI and robotics, Guan is helping to shape a new paradigm where robots can learn more flexible, generalizable manipulation skills, making her a rising voice in the field.
Research Focus
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Top Papers
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