My research aims to develop and unify techniques from both
nonlinear control theory and machine learning
to systematically achieve stable and robust robotic-assisted locomotion. This includes developing efficient methods of user customization via human-robot interaction as well as studying the efficacy of assisted locomotion in clinical settings.


  1. Synthesizing Robust Walking Gaits via Discrete-Time Barrier Functions with Application to Multi-Contact Exoskeleton Locomotion
    Maegan Tucker, Kejun Li, and Aaron D Ames
    In In Review 2023
  2. Humanoid Robot Co-Design: Coupling Hardware Design with Gait Generation via Hybrid Zero Dynamics
    Adrian B Ghansah, Jeeseop Kim, Maegan Tucker, and 1 more author
    In 2023 IEEE Conference on Decision and Control (CDC) 2023
  3. An input-to-state stability perspective on robust locomotion
    Maegan Tucker, and Aaron D Ames
    IEEE Control Systems Letters 2023
  4. Input-to-State Stability in Probability
    Preston Culbertson, Ryan K Cosner, Maegan Tucker, and 1 more author
    In 2023 IEEE Conference on Decision and Control (CDC) 2023
  5. Learning controller gains on bipedal walking robots via user preferences
    Noel Csomay-Shanklin, Maegan Tucker, Min Dai, and 2 more authors
    In 2022 International Conference on Robotics and Automation (ICRA) 2022
  6. Natural Multicontact Walking for Robotic Assistive Devices via Musculoskeletal Models and Hybrid Zero Dynamics
    Kejun Li, Maegan Tucker, Rachel Gehlhar, and 2 more authors
    IEEE Robotics and Automation Letters 2022
  7. Robust Locomotion: Leveraging Saltation Matrices for Gait Optimization
    Maegan Tucker, Noel Csomay-Shanklin, and Aaron D Ames
    arXiv preprint arXiv:2209.10452 2022
  8. Stabilization of Exoskeletons through Active Ankle Compensation
    Thomas Gurriet, Maegan Tucker, Claudia Kann, and 2 more authors
    arXiv preprint arXiv:1909.11848 2019


  1. Leveraging user preference in the design and evaluation of lower-limb exoskeletons and prostheses
    Kimberly A Ingraham, Maegan Tucker, Aaron D Ames, and 2 more authors
    Current Opinion in Biomedical Engineering 2023
  2. Safety-Aware Preference-Based Learning for Safety-Critical Control
    Ryan Cosner, Maegan Tucker, Andrew Taylor, and 7 more authors
    In Learning for Dynamics and Control Conference 2022
  3. ROIAL: Region of interest active learning for characterizing exoskeleton gait preference landscapes
    Kejun Li, Maegan Tucker, Erdem Bıyık, and 6 more authors
    In 2021 IEEE International Conference on Robotics and Automation (ICRA) 2021
  4. Preference-based learning for user-guided HZD gait generation on bipedal walking robots
    Maegan Tucker, Noel Csomay-Shanklin, Wen-Loong Ma, and 1 more author
    In 2021 IEEE International Conference on Robotics and Automation (ICRA) 2021
  5. Human preference-based learning for high-dimensional optimization of exoskeleton walking gaits
    Maegan Tucker, Myra Cheng, Ellen Novoseller, and 4 more authors
    In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020
  6. Preference-based learning for exoskeleton gait optimization
    Maegan Tucker, Ellen Novoseller, Claudia Kann, and 4 more authors
    In 2020 IEEE international conference on robotics and automation (ICRA) 2020


  1. A review of current state-of-the-art control methods for lower-limb powered prostheses
    Rachel Gehlhar, Maegan Tucker, Aaron J Young, and 1 more author
    Annual Reviews in Control 2023
  2. Real-time feedback module for assistive gait training, improved proprioception, and fall prevention
    Maegan Tucker, and Aaron D Ames
    Jan 2021
  3. Evaluation of safety and performance of the self balancing walking system Atalante in patients with complete motor spinal cord injury
    Jacques Kerdraon, Jean Gabriel Previnaire, Maegan Tucker, and 4 more authors
    Spinal Cord Series and Cases Jan 2021
  4. Towards variable assistance for lower body exoskeletons
    Thomas Gurriet, Maegan Tucker, Alexis Duburcq, and 2 more authors
    IEEE Robotics and Automation Letters Jan 2019