Creating diverse tasks to catalyze robot learning

Data has become an essential catalyst for the development of artificial intelligence. But it is challenging to obtain data for robotic learning. So how should we tackle this issue? In this talk, we start with a retrospective of how ImageNet and other large-scale datasets incentivized the deep learning revolution in the past decade, and aim to tackle the new challenges faced by robotic data. To this end, we introduce two lines of work in the Stanford Vision and Learning Lab on creating tasks to catalyze robot learning in this new era. We first present the design of a large-scale and realistic environment in simulation that enables human and robotic agents to perform interactive tasks. We further propose a novel approach for automatically generating suitable tasks as curricula to expedite reinforcement learning in hard-exploration problems.