Papers
19
Total Citations
431
H-Index
9
About
Marion Leibold is a leading researcher in robotics and control systems, specializing in the development of advanced algorithms for robot manipulation and motion control. Her major contributions lie in model predictive control (MPC), adaptive control, and disturbance estimation for Euler-Lagrangian systems, with a focus on enhancing the versatility and dexterity of dual-arm manipulators. Her most cited work, a 2015 IEEE IROS paper with 129 citations, introduces a novel method for improving transition times between walking controllers, showcasing her impact on legged locomotion. She has also pioneered frameworks for human-like reaching motions (77 citations) and integral sliding-mode observers for precise disturbance estimation (56 citations). Her recent work on hierarchical incremental MPC and universal cooperative manipulation frameworks has garnered attention, with papers from 2024 already accumulating 32 and 24 citations, respectively. Leibold’s research bridges theoretical rigor and practical implementation, addressing challenges like unmodeled dynamics, input saturation, and singularity avoidance. Her achievements include developing model-free robust-adaptive controllers and data-driven control strategies, making her a key figure in advancing autonomous robotic systems for human-centered environments.
Research Focus
Key Achievements
Top Papers
- 12015 IEEE International Conference on Intelligent Robots and Systems (IROS)129 citations · 2015
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- 10An Online Adaptation Strategy for Direct Data-driven Control5 citations · 2023