Ted Staley
I am a senior AI engineer at Johns Hopkins University Applied Physics Laboratory (APL), where I work in reinforcement learning, foundation models, and robotics. I am interested in understanding approaches to robotic control that are general and scalable, especially using imitation learning or reinforcement learning, and leveraging data sources that have low barriers to collection. Some of my projects result in publications, which you can find on my Google Scholar.
I recently stood up this website to collect my thoughts and notes on AI for robotic control. As I continue working in this area I am hoping it becomes an online portfolio and blog. I am also maintaining repositories of my relevant code implementations on my github.
I currently teach ChatGPT from Scratch at the Johns Hopkins Engineering for Professionals (EP) Program.
You can reach me at edward.staley@jhuapl.edu
All Recent Posts:
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
Proximal Policy Optimization (PPO)
Algorithm Review and Notes
October 1, 2024
Older Posts:
Soft Actor-Critic (SAC)
Darwinian Objectives
Scones