  A key challenge in manipulation is learning a policy that can robustly
generalize to diverse visual environments. A promising mechanism for learning
robust policies is to leverage video generative models, which are pretrained on
large-scale datasets of internet videos. In this paper, we propose a visuomotor
policy learning framework that fine-tunes a video diffusion model on human
demonstrations of a given task. At test time, we generate an example of an
execution of the task conditioned on images of a novel scene, and use this
synthesized execution directly to control the robot. Our key insight is that
using common tools allows us to effortlessly bridge the embodiment gap between
the human hand and the robot manipulator. We evaluate our approach on four
tasks of increasing complexity and demonstrate that harnessing internet-scale
generative models allows the learned policy to achieve a significantly higher
degree of generalization than existing behavior cloning approaches.
