Jacob Levy

I am a final year robotics PhD student at the University of Texas at Austin, where I am advised by David Fridovich-Keil. I am interested in developing algorithms for robotic systems which efficiently learn from limited real-world experience to rapidly adapt to unmodeled dynamics and unseen environments. My research lies at the intersection of world models, reinforcement learning (RL), and adaptive control. I place a strong emphasis on real-world experiments and practical applicability. My research is funded though the NASA NSTRGO fellowship.

Prior to starting my PhD, I worked for 10 years at Parker Aerospace as an Engineering Test Lab Manager and Test Engineer. I have a M.S. in Aerospace Engineering from the University of Texas at Austin and a B.S. in Aerospace Engineering from the University of Texas at Arlington.

Email / CV / LinkedIn / Google Scholar

Selected Research

	Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation Jacob Levy, Tyler Westenbroek, Kevin Huang, Fernando Palafox, Patrick Yin, Shayegan Omidshafiei, Dong-Ki Kim, Abhishek Gupta, David Fridovich-Keil RSS 2026 (Accepted) arxiv / website / code We introduce a scalable framework that distills structural priors from a simulator into a latent world model and enables rapid real-world adaptation via online planning and supervised dynamics finetuning.
	Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving Jacob Levy, Jason Gibson, Bogdan Vlahov, Erica Tevere, Evangelos Theodorou, David Fridovich-Keil, Patrick Spieler RSS 2025 arxiv / video We develop an online adaptation algorithm for autonomous off-road vehicles in unknown environments and show how meta-learning improves adaptation speed and robustness.
	Learning to Walk from Three Minutes of Real-World Data with Semi-structured Dynamics Models Jacob Levy, Tyler Westenbroek, David Fridovich-Keil CoRL 2024 arxiv / website / code We train a quadruped to walk with only 3 minutes of real-world data by leveraging known Lagrangian dynamics and learned contact models with model-based RL.
	Enabling Efficient, Reliable Real-World Reinforcement Learning with Approximate Physics-Based Models Tyler Westenbroek, Jacob Levy, David Fridovich-Keil CoRL 2023 arxiv / code We develop a real-world reinforcement learning framework that leverages approximate physics models and embedded feedback control to learn robot policies with minutes of real-world data.

Other

go2_isaac_ros2
code

This package allows low-level (or joint-level), ROS2 control of a Unitree Go2 quadruped robot being simulated in Isaac Sim.

Template adapted from jonbarron.github.io.