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 IEEE International Conference on Robotics and Automation (ICRA) 2024


  1. Humanoid Robot Co-Design: Coupling Hardware Design with Gait Generation via Hybrid Zero Dynamics
    Adrian B Ghansah, Jeeseop Kim, Maegan Tucker, and Aaron D Ames
    In IEEE Conference on Decision and Control (CDC) 2023
  2. Leveraging user preference in the design and evaluation of lower-limb exoskeletons and prostheses
    Kimberly A Ingraham, Maegan Tucker, Aaron D Ames, Elliott J Rouse, and Max K Shepherd
    Current Opinion in Biomedical Engineering 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 Aaron D Ames
    In IEEE Conference on Decision and Control (CDC) 2023
  5. A review of current state-of-the-art control methods for lower-limb powered prostheses
    Rachel Gehlhar, Maegan Tucker, Aaron J Young, and Aaron D Ames
    Annual Reviews in Control 2023


  1. Learning controller gains on bipedal walking robots via user preferences
    Noel Csomay-Shanklin, Maegan Tucker, Min Dai, Jenna Reher, and Aaron D Ames
    In International Conference on Robotics and Automation (ICRA) 2022
  2. Natural Multicontact Walking for Robotic Assistive Devices via Musculoskeletal Models and Hybrid Zero Dynamics
    Kejun Li, Maegan Tucker, Rachel Gehlhar, Yisong Yue, and Aaron D Ames
    IEEE Robotics and Automation Letters 2022
  3. Safety-Aware Preference-Based Learning for Safety-Critical Control
    Ryan Cosner, Maegan Tucker, Andrew Taylor, Kejun Li, Tamas Molnar, Wyatt Ubellacker, Anil Alan, Gábor Orosz, Yisong Yue, and Aaron Ames
    In Learning for Dynamics and Control Conference 2022
  4. Robust Locomotion: Leveraging Saltation Matrices for Gait Optimization
    Maegan Tucker, Noel Csomay-Shanklin, and Aaron D Ames
    arXiv preprint arXiv:2209.10452 2022


  1. ROIAL: Region of interest active learning for characterizing exoskeleton gait preference landscapes
    Kejun Li, Maegan Tucker, Erdem Bıyık, Ellen Novoseller, Joel W Burdick, Yanan Sui, Dorsa Sadigh, Yisong Yue, and Aaron D Ames
    In IEEE International Conference on Robotics and Automation (ICRA) 2021
  2. Preference-based learning for user-guided HZD gait generation on bipedal walking robots
    Maegan Tucker, Noel Csomay-Shanklin, Wen-Loong Ma, and Aaron D Ames
    In IEEE International Conference on Robotics and Automation (ICRA) 2021
  3. Real-time feedback module for assistive gait training, improved proprioception, and fall prevention
    Maegan Tucker, and Aaron D Ames
    Jan 2021
  4. 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, Pauline Coignard, Willy Allegre, Emmanuel Knappen, and Aaron Ames
    Spinal Cord Series and Cases Jan 2021


  1. Human preference-based learning for high-dimensional optimization of exoskeleton walking gaits
    Maegan Tucker, Myra Cheng, Ellen Novoseller, Richard Cheng, Yisong Yue, Joel W Burdick, and Aaron D Ames
    In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Jan 2020
  2. Preference-based learning for exoskeleton gait optimization
    Maegan Tucker, Ellen Novoseller, Claudia Kann, Yanan Sui, Yisong Yue, Joel W Burdick, and Aaron D Ames
    Jan 2020


  1. Stabilization of Exoskeletons through Active Ankle Compensation
    Thomas Gurriet, Maegan Tucker, Claudia Kann, Guilhem Boeris, and Aaron D Ames
    arXiv preprint arXiv:1909.11848 Jan 2019
  2. Towards variable assistance for lower body exoskeletons
    Thomas Gurriet, Maegan Tucker, Alexis Duburcq, Guilhem Boeris, and Aaron D Ames
    IEEE Robotics and Automation Letters Jan 2019

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