In this talk we'll discuss different views on representations for robot learning, in particular towards the goal of precise, generalizable vision-based manipulation skills that are sample-efficient and scalable to train. Object-centric representations, on the one hand, can enable using rich additional sources of learning, and can enable various efficient downstream behaviors. Action-centric representations, on the other hand, can learn high-level planning, and do not have to explicitly instantiate objectness. As case studies we’ll look at two recent papers in these two areas.