While there has been major investment in developing large-scale robot learning algorithms, achieving true autonomy remains an open research challenge. Key ingredients towards this goal are a robot’s ability to generalize and efficiently adapt to unseen scenarios and new tasks variations, the ability to recover from failures, and to efficiently learn an entirely new skill from human demonstrations. In this talk, I’ll present examples of FAIR robotics research towards the goal of learning general representations for a wide spectrum of robotics applications.

- part of Session 3 "Generative AI in Robotics and Big Data" -