Data-Efficient Robot Learning
We are developing a theory and framework for data-efficient robot learning that unifies efficient representation learning with provably reliable supervision. Our goal is to enable agents to generalize from limited, imperfect, or synthetic data by grounding both what they learn, through compact, task-aligned representations, and how they learn, through corrective labels that are mathematically consistent with the underlying dynamics. This perspective treats learning as an interplay between structure discovery and label synthesis, yielding algorithms that can extrapolate safely beyond expert demonstrations. Ultimately, we aim to build robotic systems that learn robustly and efficiently from sparse, weak, or self-generated experience.
Published contributors
- Siddhartha Srinivasa
- Ethan Pronovost
- Yunchu Zhang
- Quinn Pfeifer
- Liyiming Ke
- Abhay Deshpande
Publications
BibTeX of these publications (.bib)