Papers
4
Total Citations
37
H-Index
4
About
Qinwen Li’s research bridges the gap between robotic manipulation and adaptive control, focusing on how machines can safely and intelligently interact with unknown environments. Her key contributions lie in impedance estimation, obstacle avoidance, and hybrid force-position control—areas critical for autonomous robots operating in unstructured settings. In her most-cited work, “Impedance estimation for robot contact with uncalibrated environments” (2021, 16 citations), Li developed a method that allows robots to estimate environmental stiffness in real time without prior calibration, enabling more compliant and stable contact during tasks like assembly or human-robot collaboration. Her 2022 paper on model predictive obstacle avoidance (10 citations) introduced a dynamic motion primitive framework combined with a Kalman filter, advancing safe navigation in dynamic surroundings. Earlier, Li explored bio-inspired adhesion with her study on gecko-inspired carbon nanotube dry adhesives (2013, 7 citations), demonstrating how flexible nanostructures can enhance gripping performance. Her 2020 work on virtual semi-active damping learning control (4 citations) further refined robot-environment interaction by eliminating the need to switch between position and force control modes. With a growing citation record and a focus on practical, uncalibrated environments, Li’s research is shaping the future of adaptive, contact-rich robotics.
Research Focus
Key Achievements
Top Papers
- 1Impedance estimation for robot contact with uncalibrated environments16 citations · 2021
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