Maegan Tucker

Hi, my name is Maegan Tucker. I’m an incoming Assistant Professor at Georgia Tech (starting January 2024) and currently a PhD candidate at Caltech, under the advisement of Dr. Aaron Ames. My research is centered around achieving stable and user-preferred locomotion on robotic assistive devices using formal methods from both nonlinear control theory and machine learning. My research has been supported by an NSF Graduate Research Fellowship and the Caltech RoAMS initiative.

Research Overview

Aside from my research, I am deeply passionate about furthering DEI efforts within the robotics community. Towards this, I am involved in several Caltech DEI efforts such as Future Ignited, Freshman Summer Research Institute (FSRI), and Rise Tutoring. If you are interested in discussing either my research or wish to become involved in DEI initiatives, please reach out at mtucker@caltech.edu!

recent publications

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

arXiv Bib PDF Video

@inproceedings{csomay2022learning,
  title = {Learning controller gains on bipedal walking robots via user preferences},
  author = {Csomay-Shanklin, Noel and Tucker, Maegan and Dai, Min and Reher, Jenna and Ames, Aaron D},
  booktitle = {2022 International Conference on Robotics and Automation (ICRA)},
  pages = {10405--10411},
  year = {2022},
  organization = {IEEE},
}

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

arXiv Bib PDF Video

@article{li2022natural,
  title = {Natural Multicontact Walking for Robotic Assistive Devices via Musculoskeletal Models and Hybrid Zero Dynamics},
  author = {Li, Kejun and Tucker, Maegan and Gehlhar, Rachel and Yue, Yisong and Ames, Aaron D},
  journal = {IEEE Robotics and Automation Letters},
  volume = {7},
  number = {2},
  pages = {4283--4290},
  year = {2022},
  publisher = {IEEE},
}

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

arXiv Bib PDF Video

@inproceedings{cosner2022safety,
  title = {Safety-Aware Preference-Based Learning for Safety-Critical Control},
  author = {Cosner, Ryan and Tucker, Maegan and Taylor, Andrew and Li, Kejun and Molnar, Tamas and Ubelacker, Wyatt and Alan, Anil and Orosz, G{\'a}bor and Yue, Yisong and Ames, Aaron},
  booktitle = {Learning for Dynamics and Control Conference},
  pages = {1020--1033},
  year = {2022},
  organization = {PMLR},
}

Robust Locomotion: Leveraging Saltation Matrices for Gait Optimization

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

arXiv preprint arXiv:2209.10452 2022

arXiv Bib PDF Video

@article{tucker2022robust,
  title = {Robust Locomotion: Leveraging Saltation Matrices for Gait Optimization},
  author = {Tucker, Maegan and Csomay-Shanklin, Noel and Ames, Aaron D},
  journal = {arXiv preprint arXiv:2209.10452},
  year = {2022},
}