Papers
7
Total Citations
169
H-Index
6
About
Dongdong Qin is a leading researcher in robotics and multi-agent systems, with key contributions in impedance control, model predictive control (MPC), and human-robot skill transfer. His work focuses on enabling robots to adaptively balance precision and compliance during contact-rich tasks—a critical challenge for flexible manufacturing. His most-cited paper, "Model Predictive Variable Impedance Control of Manipulators for Adaptive Precision-Compliance Tradeoff" (2022, 62 citations), introduces a novel framework that dynamically adjusts impedance parameters, significantly enhancing manipulator performance in complex environments. Qin also advances formation control and cooperative robotics, as seen in his 2020 paper on mobile robot systems (48 citations), which integrates primal-dual neural networks with distributed predictive approaches. His research on learning from demonstration (LfD), such as the unscented MPC approach for wheeled mobile manipulators (2022, 13 citations), bridges the gap between human demonstrations and autonomous robot skills. Additionally, his work on constrained variable impedance control using quadratic programming (2022, 8 citations) and neural-shaped Lyapunov functions for skill transfer (2023, 7 citations) underscores his commitment to safe, stable, and generalizable robotic systems. With over 150 citations across his portfolio, Qin is shaping the future of adaptive, human-centric robotics.
Research Focus
Key Achievements
Top Papers
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- 5Constrained Variable Impedance Control using Quadratic Programming8 citations · 2022
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