Generative AI has led to stunning successes in recent years but is fundamentally limited by the amount of data available. 
This is especially challenging in robotics, where data is often missing and especially difficult to acquire.  In this talk, I’ll introduce 
the idea of compositional generative modeling, which can significantly reduce data requirements by building complex generative models 
from smaller constituents. First, I introduce the idea of modeling generative models with energy landscapes and illustrate how they enable 
compositional generative modeling. I’ll then illustrate how such compositional models enable the synthesis of robotic plans for novel environments
 as well as complex visual scenes. Finally, I'll show how such compositionality can be applied to multiple “foundation models” to construct robotic 
systems that can hierarchically plan and reason with multimodal inputs to solve long-horizon problems.