Ted Staley
I am a senior AI engineer at Johns Hopkins University Applied Physics Laboratory (APL), where I work in AI for robotics. More broadly my work has covered reinforcement learning, language models, and imitation learning. I am interested in understanding approaches to robotic control that are general and scalable, and leverage data sources that have low barriers to collection. Some of my projects result in publications, which you can find on my Google Scholar.
I stood up this website to collect my thoughts and notes on AI for robotic control and to track my publicly released projects and publications. I am also maintaining repositories of my relevant code implementations on my github.
I previously taught ChatGPT from Scratch at the Johns Hopkins Engineering for Professionals (EP) Program, a very deep dive into building LLMs in PyTorch.
You can reach me at edward.staley@jhuapl.edu
All Recent Posts:
LLMs, MatSci, NeurIPS 2025
Coupling GPT with Materials Synthesis Simulation
March 12, 2026
GAIL with Pixels Only
Rewarding for Visual Fidelity
May 16, 2025
GAIL
Rewarding for Fidelity
April 29, 2025
MuJoCo Cronenbergs
(Mis)Adventures in Style Transfer, Part 2
February 10, 2025
MuJoCo CycleGAN
(Mis)Adventures in Style Transfer, Part 1
January 27, 2025
Flowing with Fewer Steps
Shortcut Models Notes and Review
December 12, 2024
Going with the Flow
Notes on Flow Matching (Policies)
December 9, 2024
Modeling the World
RSSM & TSSM Notes and Experiments
December 1, 2024
Diffusion Policy Part 3
Playing CarRacing-v3 with Diffusion
November 1, 2024
Diffusion Policy Part 2
Generating Images
October 30, 2024
Diffusion Policy Part 1
How does Diffusion Work?
October 20, 2024
Orange Basque Cheesecake
With Orange Syrup
October 6, 2024
Older Posts:
Proximal Policy Optimization (PPO)
Soft Actor-Critic (SAC)
Darwinian Objectives
Scones