Papers
24
Total Citations
382
H-Index
11
About
Anqing Duan is a robotics researcher whose work spans robot learning, deformable object manipulation, human-robot collaboration, and assistive healthcare robotics. With a growing body of highly cited work, Duan has established himself as a leading voice in advancing intelligent, human-centric robotic systems for both industrial and domestic applications. Among his most significant contributions is his pioneering research on deformable linear object (DLO) manipulation, where his keypoint-based bimanual shaping framework (42 citations) and topological latent control model (39 citations) offer innovative solutions to the notoriously challenging problem of robotic handling of cables and wires. His work on human-in-the-loop robot learning for smart manufacturing (43 citations) bridges the gap between machine autonomy and human expertise, emphasizing real-time adaptability in production environments. Duan has also made notable strides in healthcare robotics, developing optimization-based variable impedance control for ultrasound-guided scoliosis assessment (35 citations). His foundational contributions to imitation learning — including bio-inspired mirror neuron frameworks, structured prediction approaches, and constrained Dynamic Movement Primitives — reflect a broad commitment to enabling robots to learn efficiently from human demonstrations. Collectively, his research represents a compelling fusion of learning theory and real-world robotic application.
Research Focus
Key Achievements
Top Papers
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- 8Constrained DMPs for Feasible Skill Learning on Humanoid Robots21 citations · 2018
- 9A structured prediction approach for robot imitation learning15 citations · 2023
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