일시: 2025. 10. 1. (수요일), 오전 9시 30분~

장소: 제4공학관, D504

Speaker: Karl Pertsch, Ph.D. (Physical Intelligence)

Title: Scalable Architectures for Robot Learning

Abstract:

In this talk, I will give an overview of modern approaches for training foundation models for robotics. I will cover the design space of vision-language-action models (VLAs), that underpin most recent robot manipulation results like Pi0 and Pi0.5, and explain the modeling advances we developed over the last 1.5 years that made these possible.

Bio:

Karl Pertsch is a member of the technical staff at Physical Intelligence. Before, he was a postdoc at UC Berkeley and Stanford, working with Sergey Levine and Chelsea Finn. His work focuses on building generalist robot policies that can solve a wide range of physical manipulation tasks in the real world. Karl obtained his PhD from USC, advised by Joseph Lim. During his PhD he interned at MetaAI and Google Brain. His work has been awarded the Best Conference Paper Award at ICRA'24, two Outstanding Paper Awards Finalists at CoRL'24, and a Best Paper Finalist at RSS'25.