Yuda Fan

Papers

Total Citations

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About

Yuda Fan is a robotics researcher whose work lies at the intersection of computer vision, reinforcement learning, and robotic manipulation. His primary research focus is on developing intelligent grasping systems that enable robots to operate effectively in cluttered, real-world environments. Fan’s most notable contribution is the introduction of a transferable active grasping framework, which combines 3D vision architectures with reinforcement learning to allow a robot to autonomously search for optimal viewpoints using a hand-mounted RGB-D camera. This approach directly addresses the challenge of partial occlusion, a persistent bottleneck in robotic vision. The associated work, "Transferable Active Grasping and Real Embodied Dataset," has accumulated 22 citations, underscoring its growing influence in the field. By creating a real embodied dataset, Fan has also provided a valuable benchmark for the community, bridging the gap between simulation and real-world deployment. His research is particularly impactful for students and researchers interested in embodied AI, active perception, and the practical application of RL in robotics.