Maegan Tucker

According to our database, Maegan Tucker authored at least 18 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Materials Matter: Investigating Functional Advantages of Bio-Inspired Materials via Simulated Robotic Hopping.

Andrew K. Schulz, Ayah G. Ahmad, Maegan Tucker

CoRR, 2024

Preferential Multi-Objective Bayesian Optimization.

Raul Astudillo, Kejun Li, Maegan Tucker, Chu Xin Cheng, Aaron D. Ames, Yisong Yue

CoRR, 2024

Synthesizing Robust Walking Gaits via Discrete-Time Barrier Functions with Application to Multi-Contact Exoskeleton Locomotion.

Maegan Tucker, Kejun Li, Aaron D. Ames

Proceedings of the IEEE International Conference on Robotics and Automation, 2024

2023

A review of current state-of-the-art control methods for lower-limb powered prostheses.

Rachel Gehlhar, Maegan Tucker, Aaron J. Young, Aaron D. Ames

Annu. Rev. Control., January, 2023

An Input-to-State Stability Perspective on Robust Locomotion.

Maegan Tucker, Aaron D. Ames

IEEE Control. Syst. Lett., 2023

Robust Bipedal Locomotion: Leveraging Saltation Matrices for Gait Optimization.

Maegan Tucker, Noel Csomay-Shanklin, Aaron D. Ames

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

Humanoid Robot Co-Design: Coupling Hardware Design with Gait Generation via Hybrid Zero Dynamics.

Adrian B. Ghansah, Jeeseop Kim, Maegan Tucker, Aaron D. Ames

Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

Input-to-State Stability in Probability.

Preston Culbertson, Ryan K. Cosner, Maegan Tucker, Aaron D. Ames

Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

2022

Natural Multicontact Walking for Robotic Assistive Devices via Musculoskeletal Models and Hybrid Zero Dynamics.

Kejun Li, Maegan Tucker, Rachel Gehlhar, Yisong Yue, Aaron D. Ames

IEEE Robotics Autom. Lett., 2022

POLAR: Preference Optimization and Learning Algorithms for Robotics.

Maegan Tucker, Kejun Li, Yisong Yue, Aaron D. Ames

CoRR, 2022

Safety-Aware Preference-Based Learning for Safety-Critical Control.

Ryan K. Cosner, Maegan Tucker, Andrew J. Taylor, Kejun Li, Tamás G. Molnár, Wyatt Ubellacker, Anil Alan, Gábor Orosz, Yisong Yue, Aaron D. Ames

Proceedings of the Learning for Dynamics and Control Conference, 2022

Learning Controller Gains on Bipedal Walking Robots via User Preferences.

Noel Csomay-Shanklin, Maegan Tucker, Min Dai, Jenna Reher, Aaron D. Ames

Proceedings of the 2022 International Conference on Robotics and Automation, 2022

2021

Preference-Based Learning for User-Guided HZD Gait Generation on Bipedal Walking Robots.

Maegan Tucker, Noel Csomay-Shanklin, Wen-Loong Ma, Aaron D. Ames

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes.

Kejun Li, Maegan Tucker, Erdem Biyik, Ellen R. Novoseller, Joel W. Burdick, Yanan Sui, Dorsa Sadigh, Yisong Yue, Aaron D. Ames

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

2020

Towards Variable Assistance for Lower Body Exoskeletons.

Thomas Gurriet, Maegan Tucker, Alexis Duburcq, Guilhem Boeris, Aaron D. Ames

IEEE Robotics Autom. Lett., 2020

Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits.

Maegan Tucker, Myra Cheng, Ellen R. Novoseller, Richard Cheng, Yisong Yue, Joel W. Burdick, Aaron D. Ames

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

Preference-Based Learning for Exoskeleton Gait Optimization.

Maegan Tucker, Ellen R. Novoseller, Claudia Kann, Yanan Sui, Yisong Yue, Joel W. Burdick, Aaron D. Ames

Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

2019

Stabilization of Exoskeletons through Active Ankle Compensation.

Thomas Gurriet, Maegan Tucker, Claudia Kann, Guilhem Boeris, Aaron D. Ames

CoRR, 2019