  We pursue the goal of developing robots that can interact zero-shot with
generic unseen objects via a diverse repertoire of manipulation skills and show
how passive human videos can serve as a rich source of data for learning such
generalist robots. Unlike typical robot learning approaches which directly
learn how a robot should act from interaction data, we adopt a factorized
approach that can leverage large-scale human videos to learn how a human would
accomplish a desired task (a human plan), followed by translating this plan to
the robots embodiment. Specifically, we learn a human plan predictor that,
given a current image of a scene and a goal image, predicts the future hand and
object configurations. We combine this with a translation module that learns a
plan-conditioned robot manipulation policy, and allows following humans plans
for generic manipulation tasks in a zero-shot manner with no deployment-time
training. Importantly, while the plan predictor can leverage large-scale human
videos for learning, the translation module only requires a small amount of
in-domain data, and can generalize to tasks not seen during training. We show
that our learned system can perform over 16 manipulation skills that generalize
to 40 objects, encompassing 100 real-world tasks for table-top manipulation and
diverse in-the-wild manipulation. https://homangab.github.io/hopman/
