Research
We work on robot learning — developing algorithms that enable robots to learn and act in the real world. Our work spans the following directions.
Data-efficient Robot Learning
We exploit the geometric structure of robotics tasks — symmetry and equivariance (e.g. SE(3)) — so that models learn far more data-efficiently and generalize from only a handful of demonstrations.
Robot Foundation Models
To generalize across diverse real-world tasks, we leverage foundation models pre-trained on large-scale data from many domains — vision, language, and video — to build Vision-Language-Action (VLA) models and World-Action Models (WAM) for robots.
Task Planning with Prior Knowledge
We make robot task planning more efficient by injecting prior knowledge (e.g. commonsense knowledge) represented with structures such as ontologies — so robots reason about what to do with far less search and supervision.