Research
I equip robots with foundational motion skills. Recently I've been especially interested in sim-to-real transfer: how we can ensure that behaviors learned in simulation will work in the real world.
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SoftMimic: Learning Compliant Whole-body Control from Examples
Gabriel B. Margolis*,
Michelle Wang*,
Nolan Fey,
Pulkit Agrawal
ICRA 2026
Variable-stiffness humanoid motion imitation enabling gentle contact with the environment for safety and generalization.
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Bridging the Sim-to-Real Gap for Athletic Loco-Manipulation
Nolan Fey, Gabriel B. Margolis, Martin Peticco, Pulkit Agrawal
RSS 2025; Robot Learning Workshop, 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
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A Learning-based Framework to Adapt Legged Robots On-the-fly to Unexpected Disturbances
Nolan Fey, He Li, Nicholas Adrian, Patrick Wensing, Michael Lemmon
L4DC, 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.
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3D Hopping in Discontinuous Terrain Using Impulse Planning with Mixed-Integer Strategies
Nolan Fey, Robert Frei, Patrick Wensing
IEEE RA-L, 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.
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Video (IEEE)
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Code
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