Maegan received her PhD in Mechanical Engineering (ME) from the California Institute of Technology (Caltech) in May 2023. Prior, she also received a M.S. in ME from Caltech in 2019 and a B.S. in ME from Georgia Tech in 2017. After graduating with her PhD, Maegan conducted a brief postdoc at Caltech (May - August 2023), followed by a brief research position at Disney Research (September - December 2023). Generally speaking, her research interests lie at the intersection of control theory and human-robot interaction, with specific applications towards lower-limb assistive devices. Much of her research is centered around the question: “What is the right way to walk?”. In her free time, Maegan enjoys puzzles, playing video games, and the piano.
- Ph.D., California Institute of Technology, 2023
- M.S., California Institute of Technology, 2019
- B.S., Georgia Institute of Technology, 2017
Professor Tucker’s research lies at the intersection of robotics and control, with an emphasis on developing principled methods for analyzing and designing robotic systems that interact closely with humans. Her work spans intuitive human–robot interaction, theoretical analysis of robustness in hybrid and nonlinear control systems, and the control of wearable lower-limb exoskeletons for rehabilitation. By combining tools from geometric and nonlinear control theory with human-in-the-loop experimentation, her research seeks to ensure safety, robustness, and performance in legged and wearable robotic systems operating in real-world settings. Her research program integrates rigorous theoretical analysis with experimental validation and actively involves undergraduate and graduate students in advancing fundamental control theory alongside impactful applications in rehabilitation robotics.
Professor Tucker’s teaching interests center on the fundamentals of robotics and control, with particular emphasis on geometric control, nonlinear control theory, and hybrid systems. She is dedicated to helping students build a rigorous theoretical foundation in robot kinematics, dynamics, and feedback control, while applying these tools to challenging robotic domains such as bipedal locomotion. At both the undergraduate and graduate levels, her courses integrate mathematical rigor with hands-on experimentation, enabling students to translate abstract control concepts into practical algorithms for hybrid, underactuated, and legged robotic systems. Her teaching philosophy emphasizes problem-driven learning and principled system design to prepare students to contribute to advances in robotics and autonomous systems.
- Dean’s Jr. Professorship
- Caltech Centennial Prize for the Best Thesis in Mechanical and Civil Engineering award (2023)
- Caltech Simoudis Discovery Prize (2022)
- Berkeley ME Rising Star (2020)
- ICRA 2020 Best Overall Paper (Awarded)
- ICRA 2020 Best Paper in Human-Robot Interaction (Awarded)
- NSF Graduate Research Fellow (2019-2022)
- N.C. Janwani, V. Madabushi, M. Tucker, NaviGait: Navigating Dynamically Feasible Gait Libraries using Deep Reinforcement Learning, ICRA 2026.
- R. Astudillo, K. Li, M. Tucker, C.X. Cheng, A.D. Ames, Y. Yue, Preferential multi-objective Bayesian optimization, TMLR, 2025.
- A.K. Schulz, A.G. Ahmad, M. Tucker, Materials Matter…, ICRA 2025, 13391…, 2025.
- L.F. Shaikewitz, M. Tucker, N.C. Janwani, A.D. Ames, Front‑leg assistive exoskeleton, US Patent App. 18/422,871, 2024.
- M. Tucker, A.D. Ames, Real‑time feedback module for assistive gait training…, US Patent 12,131,814, 2024.
- Tucker, M.*, Novoseller, E.*, et al. "Preference-Based Learning for Exoskeleton Gait Optimization." In 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020. (*Denotes equal contribution) [https://arxiv.org/pdf/1909.12316.pdf]
- Tucker, M., Csomay-Shanklin, N., Ma, W., & Ames, A. D. "Preference-based learning for user-guided HZD gait generation on bipedal walking robots." In 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021. [https://arxiv.org/pdf/2011.05424.pdf]
- Tucker, M., Csomay-Shanklin, N., and Ames, A. D. “Robust Bipedal Locomotion: Leveraging Saltation Matrices for Gait Optimization.” In 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023. [https://arxiv.org/pdf/2209.10452.pdf]
- Tucker, M., and Ames, A. D. “An input-to-state stability perspective on robust locomotion.” IEEE Control Systems Letters. 2023. [https://arxiv.org/pdf/2303.10231.pdf]