Papers
8
Total Citations
202
H-Index
6
About
Hu Cao is a robotics and computer vision researcher whose work sits at the intersection of deep learning, robotic manipulation, and neuromorphic sensing. His research primarily focuses on robotic grasp detection, pose estimation, and autonomous navigation, with a particular emphasis on developing efficient, real-time systems suitable for practical deployment. Cao's most influential contribution, "Efficient Grasp Detection Network With Gaussian-Based Grasp Representation for Robotic Manipulation" (2022, 68 citations), introduced a novel Gaussian-based grasp representation that meaningfully advances the accuracy-speed trade-off in real-time grasping systems. Complementing this, his lightweight convolutional and residual squeeze-and-excitation network architectures demonstrate a consistent drive toward computationally efficient solutions without sacrificing performance. A distinctive thread in Cao's portfolio is his pioneering work in neuromorphic vision for robotics. His early contributions establishing event-based grasping datasets (2020) and his NeuroGrasp framework (2022, 35 citations) helped lay foundational groundwork for applying neuromorphic sensors to robotic manipulation — a relatively nascent but rapidly growing area. His broader interests also extend to biologically inspired localization and point set registration for mobile robots. With over 200 cumulative citations, Cao's research is shaping how robots perceive and interact with the physical world, bridging cutting-edge deep learning with next-generation sensory technologies.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6Fast and deterministic (3+1)DOF point set registration with gravity prior19 citations · 2023
- 7
- 8