Maegan Tucker
Georgia Institute of Technology, GA·Engineering
Many machines move in repeating patterns. A walking robot swings its legs in a steady rhythm, and a robotic hand repeatedly makes and breaks contact with an object.
- Fields
Engineering
Computer Science
Mathematics & Statistics
- Topics
cyber-physical systems
contraction theory
hybrid systems
- Grants
- 1 known grant, through Jun 2028
What they work on
- Extend contraction theory, a mathematical test of whether nearby motions of a system converge, to hybrid systems that mix smooth motion with sudden impacts. Use it to estimate regions of attraction for contact-rich robots.
- Design computation-aware algorithms that calculate regions of attraction, the starting states from which a robot recovers its intended motion, efficiently enough for practical motion synthesis in complex periodic systems.
- Validate the full contraction-based framework experimentally on contact-rich mechanical systems, including bipedal walking robots and multi-fingered robotic hands.
- Generate periodic motions that stay robust to model errors, environmental uncertainty and external disturbances, while using the robot's natural dynamics to reduce the energy needed for stabilization.
More about the research
These motions need to stay stable when the robot's model is imperfect, when the ground is unexpected, or when the robot gets pushed. Designing them is hard, and the computation quickly becomes unmanageable as machines get more complex. This project tackles that problem for cyber-physical systems, machines whose computers are tightly linked to their physical surroundings. Examples range from robotic dogs to traffic control and chemical plants.
Why it matters
Walking robots and robotic hands must keep working when their models are imperfect and when they are pushed or meet unexpected contact. Robustness guarantees that scale and are easy to interpret could make such machines more reliable. Because the methods use a machine's natural dynamics, they could also make these machines less energy-hungry to stabilize. The same tools may apply to other systems with repeating behavior, such as traffic flow control and chemical plants.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Guanya Shi
Carnegie Mellon University, PA·Engineering
Develop finite-time and long-run convergence theories that show when sampling-based optimal control reaches the best solution and how its performance scales with algorithm and system parameters.
Ioannis Kantaros
Washington University in St Louis, MO·Engineering·Early-career
Train multi-agent reinforcement learning (MARL) control policies for robot teams and mathematically certify their robustness against adversarial, high-dimensional sensor inputs, testing them on aerial and ground robot testbeds and photorealistic simulators.
Nathan Usevitch
Brigham Young University, UT·Engineering
Develop and experimentally test models of the energy a flywheel-powered jumping robot can store, based on flywheel shape, size, material, motor power, surrounding fluid and payload.
The school
Georgia Institute of Technology: 50 labs on PI Pages, $1.5B a year on research, 572 research doctorates a year.
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