Papers
33
Total Citations
822
H-Index
16
About
Kaiyu Hang is a robotics researcher whose work sits at the intersection of robotic grasping, dexterous manipulation, and autonomous planning. With a research trajectory spanning over a decade, Hang has made foundational contributions to how robots perceive, grasp, and reposition objects in complex, uncertain environments. His early work on hierarchical fingertip spaces and dexterous grasping under shape uncertainty — now boasting over 100 citations — established principled frameworks for precision grasp synthesis when object geometry is incompletely known. Building on this foundation, Hang tackled increasingly sophisticated manipulation challenges: enabling robots to slide thin objects into graspable positions using compliant hands, perform finger gaiting to extend in-hand dexterity beyond kinematic constraints, and self-identify hand-object parameters for more controllable manipulation. Beyond grasping, Hang has advanced nonprehensile manipulation — pushing and rearranging objects without firmly holding them — using deep reinforcement learning, simulation-to-reality transfer, and Monte Carlo Tree Search. His benchmarking contributions in both grasp planning and in-hand manipulation have provided the community with standardized evaluation tools that are widely adopted. A recurring theme across his portfolio is making robotic manipulation robust, generalizable, and measurable — qualities that continue to shape the field's research agenda.
Research Focus
Key Achievements
Top Papers
- 1Dexterous grasping under shape uncertainty106 citations · 2015
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- 6Benchmarking In-Hand Manipulation48 citations · 2020
- 7Benchmarking Protocol for Grasp Planning Algorithms41 citations · 2019
- 8Hierarchical Fingertip Space for multi-fingered precision grasping41 citations · 2014
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