Ailing Li

Papers

Total Citations

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About

Ailing Li is a robotics researcher whose work bridges the gap between autonomous manipulation and medical intervention. Her primary research areas include robotic grasping, deep learning for control systems, and robot-assisted surgery. Li’s major contributions center on developing stable, intelligent grasping algorithms using deep neural networks (DNNs), where she derived optimal grasping cost functions from probability density distributions to enable multi-object handling. Her 2020 paper on this topic has garnered 10 citations, laying foundational work for adaptive robotic hands. In the medical domain, Li advanced robot-assisted needle insertion for CT-guided puncture, validated through experimental studies with phantoms and animals (10 citations). She also integrated deep learning with Smith predictors to overcome time delays in remote grasping control systems (7 citations). This work demonstrates her ability to combine theoretical control methods with practical robotic applications. Li’s research is notable for its direct impact on both industrial automation and minimally invasive surgery, showcasing her versatility in solving real-world manipulation challenges. Her citation record reflects growing recognition in the robotics community for her innovative, application-driven approach.