Nolan Fey

I'm a Ph.D. student in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT, where I'm advised by Pulkit Agrawal. My research focuses on designing whole-body control methods that enable robots to intelligently move through and interact with the world. I'm grateful to be supported by the NSF GRFP.

Previously, I received a B.S. in Electrical Engineering with a Second Major in Physics at Notre Dame, where I worked with Patrick Wensing and Michael Lemmon. I've also been fortunate to intern at the U.S. Naval Research Lab in D.C. and NASA JPL.

Email  /  Google Scholar  /  X  /  GitHub

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Research

I equip robots with foundational motion skills that are broadly applicable to a wide range of tasks. The tools I develop are easily adaptable to new robots and environments, paving the way for future applications that demand significant mobility and control—whether it’s a construction robot, a cave explorer on distant planets, or a home robot that can help you move a couch!

Unitree B2/Z1 throwing ball Bridging the Sim-to-Real Gap for Athletic Loco-Manipulation
Nolan Fey, Gabriel B. Margolis, Martin Peticco, Pulkit Agrawal
Workshop on Robot Learning, ICLR 2025 (Oral)

Enhancing the sim-to-real transfer for a legged manipulator by calibrating actuator dynamics and refining whole-body control, enabling agile feats like throwing, lifting, and dragging


Paper / Website / X

MIT Mini Cheetah running on treadmill while carrying a case of water A Learning-based Framework to Adapt Legged Robots On-the-fly to Unexpected Disturbances
Nolan Fey, He Li, Nicholas Adrian, Patrick Wensing, Michael Lemmon
International Conference on Learning for Dynamics & Control, 2024

A learning-based framework that allows a walking robot to stabilize itself under disturbances neglected by its base controller. We applied it to stabilize the MIT Mini Cheetah as it carried a box of water on its back.


Paper / Video

MIT Mini Cheetah mid hop 3D Hopping in Discontinuous Terrain Using Impulse Planning with Mixed-Integer Strategies
Nolan Fey, Robert Frei, Patrick Wensing
IEEE Robotics and Automation Letters, 2024.

By approximating each of the robot's stance phases to be impulsive, we enable a mixed-integer program to quickly plan consecutive hops for a quadruped between surfaces while avoiding obstacles.


Paper / Video (IEEE) / Video (YouTube) / Code