I am interested in developing machine intelligence. Towards this goal, my research sits at the intersection of reinforcement learning and statistics, and focuses on how we can use statistical tools to improve generalization in reinforcement learning. I have written about planning in robotics, diffusion models, exploration, and causal inference. Most recently, I have been investigating the intersection of large language models (LLMs) and robotics.

Short Bio

I am an assistant professor in the Northwestern University Department of Statistics and Data Science.

Previously, I was a research assistant professor at TTIC, an academic institute located on the campus of the University of Chicago. In 2018, I completed my PhD at UC Berkeley. My advisor was Pieter Abbeel. In 2016 and 2017, I was a research scientist at Open AI, where I was advised by Ilya Sutskever. I received a BA in mathematics from the University of Chicago, where I spent four wonderful years. During this time, I had the honor of working under Paul Sally.

My Google Scholar page can be found here.

My CV is here.

I recently made a YouTube video about some of my research for a general audience.

Publication Feed

2024

Solving Robotics Problems in Zero-Shot with Vision-Language Models. Available here

2023

Cold Diffusion on the Replay Buffer: Learning to Plan from Known Good States. Zidan Wang, Takuma Yoneda, Takeru Oba, Matthew Walter, Bradly C. Stadie. In Conference on Robot Learning (CoRL) 2023. Available here

To the Noise and Back: Diffusion for Shared Autonomy
Takuma Yoneda, Luzhe Sun, Ge Yang, Bradly C. Stadie, Matthew R. Walter
In Proceedings of Robotics: Science and Systems (RSS) 2023. Available here

2022

Understanding Goal Relabeling Requires Rethinking Divergence Minimization.
Lunjun Zhang, Bradly C. Stadie
In NeurIPS 2022 Deep RL workshop. Available here

Invariance Through Inference
Takuma Yoneda, Ge Yang, Matthew Walter, Bradly C. Stadie
In Robotics: Science and Systems (RSS) 2022. Available here

2021

World Model as a Graph: Learning Latent Landmarks for Planning
Lunjun Zhang, Ge Yang, Bradly C. Stadie
In International Conference on Machine Learning (ICML), Long Presentation, July 2021. Site here

2020

Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
Silviu Pitis, Harris Chan, Stephen Zhao, Bradly Stadie, Jimmy Ba
In International Conference on Machine Learning, July 2020. Paper here

Learning Intrinsic Rewards as a Bi-Level Optimization Problem
Lunjun Zhang, Bradly C. Stadie, Jimmy Ba
Conference on Uncertainty in Artificial Intelligence (UAI), July 2020. Paper here

One-Shot Pruning of Recurrent Neural Networks by Jacobian Spectrum Evaluation
Matthew Zhang, Bradly C. Stadie
In International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, April 2020. ArXiv link

2019

Transfer Learning for Estimating Causal Effects Using Neural Networks
Bradly C. Stadie, Soeren R. Kuenzel, Nikita Vemuri, Varsha Ramakrishnan, Jasjeet S. Sekhon, Pieter Abbeel
INFORMS Annual Meeting ML and causal inference workshop (2019). ArXiv link

One Demonstration Imitation Learning
Bradly C. Stadie, Siyan Zhao, Qiqi Xu, Bonnie Li, Lunjun Zhang
Preprint. See here

2018

Evolved Policy Gradients
Rein Houthooft, Richard Y. Chen, Phillip Isola, Bradly C. Stadie, Filip Wolski, Jonathan Ho, Pieter Abbeel
In Neural Information Processing Systems (NeurIPS) [Spotlight], Montreal, Canada, December 2018. ArXiv

Some Considerations on Learning to Explore via Meta-Reinforcement Learning
Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever
In Neural Information Processing Systems (NeurIPS), Montreal, Canada, December 2018. ArXiv

Learning to Learn from Flawed, Failed, and Figurative Demonstrations
Ge Yang, Bradly C. Stadie, Roberto Calandra, Pieter Abbeel, Sergey Levine, Chelsea Finn
In Neural Information Processing Systems (NeurIPS) Deep RL workshop [Spotlight], Montreal, Canada, December 2018. Paper here.

2017

Third-Person Imitation Learning
Bradly C. Stadie, Pieter Abbeel, Ilya Sutskever
In the proceedings of the International Conference on Learning Representations (ICLR), Toulon, France, April 2017. ArXiv

One-Shot Imitation Learning
Yan (Rocky) Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba
In Neural Information Processing Systems (NeurIPS), Long Beach, California, December 2017. ArXiv

2015

Incentivizing Exploration in Reinforcement Learning with Deep Predictive Models
Bradly C. Stadie, Sergey Levine, Pieter Abbeel
In Neural Information Processing Systems (NeurIPS) Deep RL Workshop, Montreal, Canada, December 2015 ArXiv