Maegan Tucker
Papers
9
Total Citations
116
H-Index
5
About
Maegan Tucker is a leading researcher in the field of robotic locomotion, specializing in the development of safe, robust, and user-friendly control systems for bipedal robots and assistive devices. Her work uniquely bridges theoretical control theory with practical human-robot interaction, focusing on lower-limb exoskeletons, prostheses, and humanoid robots. Tucker’s major contributions include pioneering the use of preference-based learning to tune controller gains for walking robots, allowing non-experts to shape robot behavior without deep domain knowledge—a breakthrough demonstrated in her 2022 work on learning controller gains via user preferences. She has also advanced robust locomotion theory by applying Input-to-State Stability (ISS) and saltation matrices to ensure bipedal robots can handle real-world disturbances, as seen in her 2023 publications. Notably, her 2021 evaluation of the Atalante self-balancing walking system in patients with complete spinal cord injury (36 citations) showcases her commitment to translating theory into clinical impact. With over 100 total citations, Tucker’s work is shaping the next generation of assistive robots that are both safer and more responsive to human needs.
Research Focus
Key Achievements
Top Papers
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- 4Input-to-State Stability in Probability10 citations · 2023
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- 6Safety-Aware Preference-Based Learning for Safety-Critical Control5 citations · 2021
- 7An Input-to-State Stability Perspective on Robust Locomotion4 citations · 2023
- 8Learning Controller Gains on Bipedal Walking Robots via User Preferences4 citations · 2022
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