Huan-ang Gao
Papers
2
Total Citations
6
H-Index
2
About
Huan-ang Gao is a rising researcher at the forefront of robotic manipulation and embodied AI, with a focus on bridging perception and physical interaction. His work centers on two key challenges: enabling dexterous pre-grasping for diverse objects and modeling part-level dynamics for world models. In his highly cited paper "PreAfford" (2024, 4 citations), Gao introduced a universal affordance-based framework that allows two-finger grippers to intelligently reposition or leverage environmental aids—like table edges—to grasp objects lacking distinct features, significantly expanding robotic adaptability across categories. This contribution addresses a critical bottleneck in real-world manipulation. More recently, in "PartRM" (2025, 2 citations), he tackles part-level dynamics modeling, moving beyond coarse video diffusion to reconstruct fine-grained, interactive object behaviors from observations and actions. This work advances the development of accurate world models for robotics and simulation. Gao’s research, though early in its citation trajectory, demonstrates high impact potential, offering practical solutions for autonomous systems. His achievements reflect a deep commitment to making robots more capable in unstructured environments, marking him as a promising voice in next-generation manipulation and scene understanding.
Research Focus
Key Achievements
Top Papers
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- 2