Title: Towards Large Behavior Models: Versatile and Dexterous Robots via Supervised Learning
Speaker: Ben Burchfiel & Siyuan Feng

Abstract: Recent advances in machine learning have transformed multiple AI-related fields. Notably, robust general-purpose language and vision models are fast becoming reality and these new capabilities have already begun making their way into consumer-facing technologies where they affect the lives of many millions of people. These same underlying advancements also portend sea-change in robotics. It is now possible to reliably imbue robots with new behaviors, such as beating eggs or folding cloth, using just an hour or two of teaching and a few dozen GPU-hours of compute. In this talk, we will discuss our team's push, at the Toyota Research Institute, to scale ML-powered robot behavior teaching and the road ahead to general-purpose Large Behavior Models for robots. These models will possess the flexibility and generality of existing Large Language Models, but will be capable of dexterously controlling a robot to effect change in the physical world.