Papers
3
Total Citations
181
H-Index
2
About
Jiayi Chen is an emerging robotics researcher whose work sits at the intersection of dexterous manipulation, deep learning, and generalizable robot control. Chen's most significant contributions center on enabling robots to grasp and interact with objects in human-like ways — a notoriously difficult challenge due to the high dimensionality of dexterous hand control and the enormous variety of real-world objects. Chen's landmark contribution, **DexGraspNet**, addressed a critical bottleneck in the field by constructing one of the first large-scale simulation datasets for dexterous robotic grasping across general object categories, laying essential groundwork for data-driven approaches. Building directly on this foundation, **UniDexGrasp** introduced a universal framework for dexterous grasping from point cloud observations, achieving diverse, high-quality grasps across hundreds of object categories — including unseen ones. Together, these two works have accumulated nearly 180 citations since 2023, signaling rapid and wide adoption by the robotics community. More recently, Chen has pushed into deformable object manipulation with **RoboHanger**, tackling the complex task of hanger insertion into garments — a problem that demands generalization across irregular, flexible shapes. Chen's trajectory reflects a clear and ambitious vision: building robotic systems capable of human-level versatility in physical manipulation.
Research Focus
Key Achievements
Top Papers
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