This event will be in-person ONLY in Wu and Chen Auditorium.

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 be deformable or articulated and vary widely in shape and appearance, while maintaining reliable performance? A popular direction 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, enabling tasks such as articulated object manipulation and precise insertion, while generalizing to unseen objects and novel configurations.