5 papers
Much Ado About Noising: Dispelling the Myths of Generative Robotic Control
Chaoyi Pan, Giri Anantharaman, Nai-Chieh Huang +8
Generative models, like flows and diffusions, have recently emerged as popular and efficacious policy parameterizations in robotics. There has been much speculation as to the facto…
Is Your Conditional Diffusion Model Actually Denoising?
Daniel Pfrommer, Zehao Dou, Christopher Scarvelis +2
We study the inductive biases of diffusion models with a conditioning-variable, which have seen widespread application as both text-conditioned generative image models and observat…
Action Chunking and Exploratory Data Collection Yield Exponential Improvements in Behavior Cloning for Continuous Control
Thomas T. Zhang, Daniel Pfrommer, Chaoyi Pan +2
This paper presents a theoretical analysis of two of the most impactful interventions in modern learning from demonstration in robotics and continuous control: the practice of acti…
A Test-Function Approach to Incremental Stability
Daniel Pfrommer, Max Simchowitz, Ali Jadbabaie
This paper presents a novel framework for analyzing Incremental-Input-to-State Stability (ISS) based on the idea of using rewards as "test functions." Whereas control theory tr…
The Pitfalls of Imitation Learning when Actions are Continuous
Max Simchowitz, Daniel Pfrommer, Ali Jadbabaie
We study the problem of imitating an expert demonstrator in a discrete-time, continuous state-and-action control system. We show that, even if the dynamics satisfy a control-theore…