Chaozheng Wu

Papers

Total Citations

124

H-Index

About

Chaozheng Wu is a leading researcher at the intersection of computer vision and robotics, whose work focuses on enabling machines to perceive and interact with the physical world. His primary research areas include visual affordance understanding and robotic grasp learning, where he has made foundational contributions. Wu is best known for developing **3D AffordanceNet** (2021, 115 citations), a benchmark that revolutionized how robots understand object interaction possibilities from 3D visual cues—categorizing, segmenting, and reasoning about affordances in a way that bridges 2D and 3D domains. This work has become a critical resource for vision-guided robotic systems. Additionally, his **Grasp Proposal Networks** (2020) introduced an end-to-end solution for learning 6-DOF robotic grasps from synthetic data, advancing the practicality of autonomous manipulation. Wu’s research directly impacts the development of more intuitive and capable robots, and his benchmark studies are widely cited by both academic and industrial teams working on embodied AI. His achievements underscore a commitment to translating visual perception into actionable robotic intelligence, making him a key figure in modern robotics research.