Stanford Seminar - Generalization through Task Representations with Foundation Models
May 23, 2025Student Speaker - Wenlong Huang, Stanford University
Building robots that can operate autonomously in unstructured environments by following arbitrary natural language commands has long been the north star in robotic manipulation. While there has been tremendous progress in learning visuomotor policies that exhibit promising signs for open-world deployment, generalization to unseen tasks or motions largely remains unattainable or out of scope. In this talk, I will discuss how deliberate choices of task representations enable such zero-shot generalization at the task level, despite given no task-specific demonstrations. Notably, I will discuss our years-long investigations into extracting task representations from off-the-shelf foundation models; I will discuss its evolution from a language-only representation to 4D space-time domain and their applications to model-based planning, affordance learning, and visuomotor policy learning. At the end of the talk, I will present an alternative view for scaling towards robotic intelligence: by leveraging foundation models to provide task-specific knowledge in the form of task representations, robotic data scaling can focus on learning from task-agnostic interactions with a world modeling objective, such that collectively this enables robots that not only understand the world as humans do but can also act within it with purpose and generality.
About the speaker: https://wenlong.page/
More about the course can be found here: https://stanfordasl.github.io/robotics_seminar/
View the entire AA289 Stanford Robotics and Autonomous Systems Seminar playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMeercb-kvGLUrOq4HR6BZD
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