Abstract:   Robots in factories are still largely limited to structured environments with known object models. How can we bring robots into the more diverse, unstructured settings of our daily lives, where objects may vary widely in shape and appearance, while maintaining reliable performance? A popular direction today is to train generalist robot policies on large-scale internet data and broad robot datasets. However, today’s generalist policies still lack the precision needed for robust real-world operation. In this talk, I argue that closing this gap requires learning a hierarchy over robot motion: learning both what subgoals to achieve as well as how to move the robot end-effector to achieve them. I will present hierarchical motion policies that combine high-level subgoal prediction with a learned low-level policy. I will show how this hierarchical approach has enabled us to achieve both generalizable and precise object manipulation.