  We tackle the problem of learning complex, general behaviors directly in the
real world. We propose an approach for robots to efficiently learn manipulation
skills using only a handful of real-world interaction trajectories from many
different settings. Inspired by the success of learning from large-scale
datasets in the fields of computer vision and natural language, our belief is
that in order to efficiently learn, a robot must be able to leverage
internet-scale, human video data. Humans interact with the world in many
interesting ways, which can allow a robot to not only build an understanding of
useful actions and affordances but also how these actions affect the world for
manipulation. Our approach builds a structured, human-centric action space
grounded in visual affordances learned from human videos. Further, we train a
world model on human videos and fine-tune on a small amount of robot
interaction data without any task supervision. We show that this approach of
affordance-space world models enables different robots to learn various
manipulation skills in complex settings, in under 30 minutes of interaction.
Videos can be found at https://human-world-model.github.io
