The Georgia Tech Dynamic Mobility (Dynamo) Lab aims to drive forward the field of bipedal locomotion and robotic-assisted mobility by developing novel techniques in the fields of control theory, human-robot interaction, and biomechanics. Overall, our group is focused on: 1) developing control techniques for achieving dynamic, natural, and robust bipedal locomotion inspired by tools and techniques from nonlinear control theory, optimization, and hybrid systems, 2) developing learning algorithms to practically adapt robotic behaviors based on human feedback, and 3) conducting clinical investigations to study the effects of robotic interventions for human rehabilitation and biomechanics.

	Bipedal Locomotion
	Learning from Human Feedback
	Biomechanics of Robot-Assisted Locomotion

Recent News

Feb 22, 2024:	One paper was accepted to the International Conference on Robotics and Automation (ICRA) 2024: - "Synthesizing Robust Walking Gaits via Discrete-Time Barrier Functions with Application to Multi-Contact Exoskeleton Locomotion"
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Recent Publications

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

video pdf arXiv bibtex

@inproceedings{tucker2023synthesizing,
  title = {Synthesizing Robust Walking Gaits via Discrete-Time Barrier Functions with Application to Multi-Contact Exoskeleton Locomotion},
  author = {Tucker, Maegan and Li, Kejun and Ames, Aaron D},
  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
  year = {2024},
}

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

video pdf arXiv bibtex

@inproceedings{ghansah2023humanoid,
  title = {Humanoid Robot Co-Design: Coupling Hardware Design with Gait Generation via Hybrid Zero Dynamics},
  author = {Ghansah, Adrian B and Kim, Jeeseop and Tucker, Maegan and Ames, Aaron D},
  booktitle = {IEEE Conference on Decision and Control (CDC)},
  year = {2023},
  organization = {IEEE},
}

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

pdf bibtex

@article{ingraham2023leveraging,
  title = {Leveraging user preference in the design and evaluation of lower-limb exoskeletons and prostheses},
  author = {Ingraham, Kimberly A and Tucker, Maegan and Ames, Aaron D and Rouse, Elliott J and Shepherd, Max K},
  journal = {Current Opinion in Biomedical Engineering},
  pages = {100487},
  year = {2023},
  publisher = {Elsevier},
}

An input-to-state stability perspective on robust locomotion

Maegan Tucker, and Aaron D Ames

IEEE Control Systems Letters 2023

pdf arXiv bibtex

@article{tucker2023input,
  title = {An input-to-state stability perspective on robust locomotion},
  author = {Tucker, Maegan and Ames, Aaron D},
  journal = {IEEE Control Systems Letters},
  year = {2023},
  publisher = {IEEE},
}

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

pdf arXiv bibtex

@inproceedings{culbertson2023input,
  title = {Input-to-State Stability in Probability},
  author = {Culbertson, Preston and Cosner, Ryan K and Tucker, Maegan and Ames, Aaron D},
  booktitle = {IEEE Conference on Decision and Control (CDC)},
  year = {2023},
  organization = {IEEE},
}