Xiaohan Yuan
Papers
1
Total Citations
3
H-Index
1
About
Xiaohan Yuan is a rising researcher at the forefront of embodied AI and robotic manipulation, with a core focus on generating realistic and functional hand-object interactions. Their most-cited work, "GraspDiff: Grasping Generation for Hand-Object Interaction With Multimodal Guided Diffusion" (2024), introduces a novel diffusion-based framework that overcomes the limitations of traditional VAE and GAN approaches. By leveraging multimodal guidance, Yuan’s method achieves both high diversity and plausibility in grasp synthesis—a critical challenge for robotics and AI-generated content. This work has already garnered early citations, signaling its impact on the field. Yuan’s contributions address a fundamental bottleneck in dexterous manipulation: generating grasps that are not only physically valid but also contextually appropriate. Their research bridges the gap between generative AI and practical robotics, offering a paradigm that prioritizes contact prediction and multimodal conditioning. As an emerging voice in this domain, Yuan is shaping how machines understand and replicate human-like interaction with objects, with implications for assistive robotics, virtual reality, and automated assembly.
Research Focus
Key Achievements
Top Papers
- 1