Fangjing Song

University of Science and Technology of China

Papers

Total Citations

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About

Fangjing Song is a leading researcher in robotic manipulation, with a focus on dexterous grasping and deep learning. Her most-cited work, "Deep Learning Method for Grasping Novel Objects Using Dexterous Hands" (2020, 35 citations), addresses a critical gap in robotics: enabling robotic hands to grasp unfamiliar objects with human-like adaptability. By modeling how humans select finger postures based on object parts, Song’s approach bridges perception and control, advancing autonomous grasping in unstructured environments. Her contributions have practical implications for manufacturing, assistive robotics, and household automation. With growing recognition in the field, Song continues to push the boundaries of dexterous manipulation, making her work essential reading for researchers tackling real-world robotic interaction challenges.