February 23, 2024
Dorsa Sadigh, Stanford University
In this talk, I will discuss how interactive robot learning can benefit from the rise of large pretrained models such as foundation models. I will introduce two perspectives. First I will discuss the role of pretraining when learning visual representations, and how language can guide learning grounded visual representations useful for downstream robotics tasks. I will then discuss the choice of datasets during pretraining. Specifically, how we could guide large scale data collection, and what constitutes high quality data for imitation learning. I will discuss some recent work around guiding data collection based on enabling compositional generalization of learned policies. Finally, I will end the talk by discussing a few creative ways of tapping into the rich context of large language models and vision-language models for robotics.
About the speaker: https://dorsa.fyi/
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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