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About

Hao-Shu Fang is a prominent robotics researcher whose work sits at the intersection of robotic manipulation, grasp perception, and embodied AI. He is best known for his groundbreaking contributions to generalizable robot grasping, a field where his research has significantly advanced the ability of robots to interact with complex, cluttered, and dynamic real-world environments. Fang's most celebrated work, **AnyGrasp** (2023, 210 citations), established a new benchmark for robust and efficient grasp perception across both spatial and temporal domains, bringing robot grasping closer to human-level capability. His earlier contributions, including **Graspness Discovery in Clutters** (2021, 124 citations) and **RGB Matters** (2021, 117 citations), introduced novel approaches to 6-DoF and 7-DoF grasp pose detection, dramatically improving speed and accuracy. His **TransCG** dataset (2022, 116 citations) addressed the underexplored challenge of grasping transparent objects, while the **GraspNet-1Billion** benchmark provided the community with rich real-world evaluation resources. Beyond grasping, Fang has contributed to large-scale robotic learning through the collaborative **Open X-Embodiment** project and explored whole-arm manipulation via low-cost exoskeletons. His cumulative citation record reflects substantial influence on how modern robotics systems perceive, plan, and execute physical interactions with the world.